{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C99P9OgY5NcH"
      },
      "source": [
        "# cifar10_resnet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:32.363026Z",
          "start_time": "2025-06-26T01:43:29.447990Z"
        },
        "id": "CTgIWCMM5NcO"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from torchvision import datasets, transforms\n",
        "from deeplearning_train import EarlyStopping, ModelSaver,train_classification_model,plot_learning_curves\n",
        "from deeplearning_train import evaluate_classification_model as evaluate_model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "IHGsTDwF5NcO"
      },
      "outputs": [],
      "source": [
        "# # 加载torchvision自带的resnet18模型\n",
        "# resnet18 = torchvision.models.resnet18()  # 加载resnet18模型\n",
        "\n",
        "# # 打印模型结构\n",
        "# print(resnet18)  # 输出模型的结构信息\n",
        "\n",
        "# from torchinfo import summary  # 导入summary函数用于显示模型结构信息\n",
        "# vgg16 = torchvision.models.vgg16()  # 加载vgg16模型\n",
        "\n",
        "# summary(vgg16, input_size=(1, 3, 224, 224), col_names=[\"input_size\", \"output_size\", \"num_params\", \"params_percent\"])  # 使用summary显示vgg16详细信息\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UOZ_jNWg5NcP"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "token = {\"username\":\"zhangyudataset\",\"key\":\"6ae9a985be19950353520e31297702b4\"}\n",
        "with open('/content/kaggle.json', 'w') as file:\n",
        "  json.dump(token, file)  # json.dump类似于write，直接把字典类型数据变为字符串写入文件\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRuuAxVn5NcP",
        "outputId": "8fe2101a-4e86-4ce3-c8ca-d020ea1b7a3d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "- path is now set to: /content\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp /content/kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle config set -n path -v /content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c8f40gk5NcP",
        "outputId": "3d55ceda-673a-4ea1-b1b4-7d0941db60c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading cifar-10.zip to /content/competitions/cifar-10\n",
            " 96% 687M/715M [00:04<00:00, 273MB/s]\n",
            "100% 715M/715M [00:05<00:00, 149MB/s]\n"
          ]
        }
      ],
      "source": [
        "# 需要先参加比赛才能下载数据集\n",
        "!kaggle competitions download -c cifar-10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aa-IOOlO5NcQ",
        "outputId": "0d5b76b1-fdc0-4ce7-b856-cf5eba44cf24"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/competitions/cifar-10/cifar-10.zip\n",
            "  inflating: sampleSubmission.csv    \n",
            "  inflating: test.7z                 \n",
            "  inflating: train.7z                \n",
            "  inflating: trainLabels.csv         \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/competitions/cifar-10/cifar-10.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B7One41g5NcQ",
        "outputId": "65e780df-dff0-42ed-ff96-fcadc9e8f3ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Collecting texttable (from py7zr)\n",
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            "\u001b[?25hDownloading texttable-1.7.0-py2.py3-none-any.whl (10 kB)\n",
            "Installing collected packages: texttable, brotli, pyzstd, pyppmd, pybcj, multivolumefile, inflate64, py7zr\n",
            "Successfully installed brotli-1.1.0 inflate64-1.0.3 multivolumefile-0.2.3 py7zr-1.0.0 pybcj-1.0.6 pyppmd-1.2.0 pyzstd-0.17.0 texttable-1.7.0\n"
          ]
        }
      ],
      "source": [
        "# 安装py7zr库，用于解压7z格式的压缩包\n",
        "%pip install py7zr  # 在Jupyter环境下安装py7zr库\n",
        "\n",
        "# 导入py7zr库\n",
        "import py7zr  # 导入py7zr模块以便后续解压操作\n",
        "\n",
        "# 创建一个SevenZipFile对象，打开'./train.7z'文件，模式为只读\n",
        "a = py7zr.SevenZipFile(r'./train.7z', 'r')  # 用于读取7z压缩包\n",
        "\n",
        "# 将压缩包中的内容全部解压到指定目录'./competitions/cifar-10/'\n",
        "a.extractall(path=r'./competitions/cifar-10/')  # 解压所有文件到目标文件夹\n",
        "\n",
        "# 关闭SevenZipFile对象，释放资源\n",
        "a.close()  # 关闭文件，完成解压流程"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pazq-6NB5NcQ"
      },
      "source": [
        "# 把数据集划分为训练集45000和验证集5000，并给DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.144223Z",
          "start_time": "2025-06-26T01:43:33.135368Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "leP4jIbc5NcR",
        "outputId": "2d94c3a4-8802-451c-f630-28cd2e6b785d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "完整数据集大小: 50000\n",
            "训练集大小: 45000\n",
            "验证集大小: 5000\n"
          ]
        }
      ],
      "source": [
        "# 加载CIFAR-10数据集相关库\n",
        "import os  # 导入os模块用于文件路径操作\n",
        "import pandas as pd  # 导入pandas用于读取csv文件\n",
        "from PIL import Image  # 导入PIL库用于图片处理\n",
        "from torch.utils.data import Dataset  # 导入PyTorch的数据集基类\n",
        "\n",
        "# 定义CIFAR-10数据集类，继承自Dataset\n",
        "class CIFAR10Dataset(Dataset):\n",
        "    def __init__(self, img_dir, labels_file, transform=None):  # 构造函数，接收图片文件夹、标签文件和预处理方法\n",
        "        self.img_dir = img_dir  # 保存图片文件夹路径\n",
        "        self.transform = transform  # 保存预处理方法\n",
        "\n",
        "        # 读取标签文件，read_csv默认第一行为列名\n",
        "        self.labels_df = pd.read_csv(labels_file)  # 读取csv标签文件\n",
        "        self.img_names = self.labels_df.iloc[:, 0].values.astype(str)  # 获取第一列图片名，转为字符串数组\n",
        "\n",
        "        # 定义类别名称到数字的映射字典\n",
        "        self.class_names_dict = {'airplane': 0, 'automobile': 1, 'bird': 2, 'cat': 3,\n",
        "                                 'deer': 4, 'dog': 5, 'frog': 6, 'horse': 7, 'ship': 8, 'truck': 9}  # 类别映射字典\n",
        "        # 将文本标签转换为数字ID\n",
        "        self.labels = [self.class_names_dict[label] for label in self.labels_df.iloc[:, 1].values]  # 标签转数字\n",
        "\n",
        "    def __len__(self):  # 返回数据集大小\n",
        "        return len(self.labels)  # 返回标签数量\n",
        "\n",
        "    def __getitem__(self, idx):  # 获取指定索引的数据\n",
        "        img_path = os.path.join(self.img_dir, self.img_names[idx] + '.png')  # 拼接图片路径\n",
        "        image = Image.open(img_path)  # 打开图片\n",
        "        label = self.labels[idx]  # 获取标签\n",
        "\n",
        "        if self.transform:  # 如果有预处理\n",
        "            image_tensor = self.transform(image)  # 对图片做预处理\n",
        "\n",
        "        return image_tensor, label  # 返回图片张量和标签\n",
        "\n",
        "# 定义数据预处理流程\n",
        "transform = transforms.Compose([  # 使用Compose组合多种预处理\n",
        "    transforms.ToTensor(),  # 转为张量\n",
        "    transforms.Normalize((0.4917, 0.4823, 0.4467), (0.2024, 0.1995, 0.2010))  # 标准化\n",
        "])\n",
        "\n",
        "# 加载CIFAR-10数据集\n",
        "img_dir = r\"competitions/cifar-10/train\"  # 图片文件夹路径\n",
        "labels_file = r\"./trainLabels.csv\"  # 标签文件路径\n",
        "full_dataset = CIFAR10Dataset(img_dir=img_dir, labels_file=labels_file, transform=transform)  # 创建完整数据集对象\n",
        "\n",
        "# 定义类别名称列表\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']  # 类别名称\n",
        "\n",
        "# 划分训练集和验证集\n",
        "train_size = 45000  # 训练集大小\n",
        "val_size = 5000  # 验证集大小\n",
        "generator = torch.Generator().manual_seed(42)  # 固定随机种子，保证可复现\n",
        "train_dataset, val_dataset = torch.utils.data.random_split(  # 随机划分数据集\n",
        "    full_dataset,  # 完整数据集\n",
        "    [train_size, val_size],  # 划分比例\n",
        "    generator=generator  # 随机数生成器\n",
        ")\n",
        "\n",
        "# 查看数据集基本信息\n",
        "print(f\"完整数据集大小: {len(full_dataset)}\")  # 打印完整数据集大小\n",
        "print(f\"训练集大小: {len(train_dataset)}\")  # 打印训练集大小\n",
        "print(f\"验证集大小: {len(val_dataset)}\")  # 打印验证集大小\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.148120Z",
          "start_time": "2025-06-26T01:43:33.145230Z"
        },
        "id": "Hgd60eaT5NcS"
      },
      "outputs": [],
      "source": [
        "def cal_mean_std(ds):\n",
        "    mean = 0.\n",
        "    std = 0.\n",
        "    for img, _ in ds:\n",
        "        mean += img.mean(dim=(1, 2)) #dim=(1, 2)表示在通道维度上求平均\n",
        "        std += img.std(dim=(1, 2))  #dim=(1, 2)表示在通道维度上求标准差\n",
        "    mean /= len(ds)\n",
        "    std /= len(ds)\n",
        "    return mean, std\n",
        "# cal_mean_std(train_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Q9Thjzwd5NcS"
      },
      "outputs": [],
      "source": [
        "# 将划分好的45000训练集和5000验证集给DataLoader\n",
        "# 创建数据加载器\n",
        "batch_size = 64\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    train_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True #打乱数据集，每次迭代时，数据集的顺序都会被打乱\n",
        ")\n",
        "\n",
        "val_loader = torch.utils.data.DataLoader(\n",
        "    val_dataset,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.152657Z",
          "start_time": "2025-06-26T01:43:33.148120Z"
        },
        "id": "PEzHf4D55NcT"
      },
      "outputs": [],
      "source": [
        "# 定义残差块类\n",
        "class ResidualBlock(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, stride=1):\n",
        "        super().__init__()\n",
        "\n",
        "        # 第一个卷积层\n",
        "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "\n",
        "        # 第二个卷积层\n",
        "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
        "\n",
        "        # 如果输入和输出通道数不同或步长不为1，则需要使用1x1卷积进行调整\n",
        "        self.shortcut = nn.Sequential()\n",
        "        if stride != 1 or in_channels != out_channels:\n",
        "            self.shortcut = nn.Sequential(\n",
        "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
        "                nn.BatchNorm2d(out_channels)\n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        residual = x\n",
        "\n",
        "        out = self.conv1(x)\n",
        "        out = self.bn1(out)\n",
        "        out = self.relu(out)\n",
        "\n",
        "        out = self.conv2(out)\n",
        "        out = self.bn2(out)\n",
        "\n",
        "        out += self.shortcut(residual) # 残差连接\n",
        "        out = self.relu(out)\n",
        "\n",
        "        return out\n",
        "\n",
        "# 定义ResNet18模型\n",
        "class ResNet18(nn.Module):\n",
        "    def __init__(self, num_classes=10):\n",
        "        super().__init__()\n",
        "\n",
        "        # 初始卷积层\n",
        "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU(inplace=True)\n",
        "\n",
        "        # 残差层\n",
        "        self.layer1 = self._make_layer(64, 64, 2, stride=1)\n",
        "        self.layer2 = self._make_layer(64, 128, 2, stride=2)\n",
        "        self.layer3 = self._make_layer(128, 256, 2, stride=2)\n",
        "        self.layer4 = self._make_layer(256, 512, 2, stride=2)\n",
        "\n",
        "        # 全局平均池化和全连接层\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.fc = nn.Linear(512, num_classes)\n",
        "\n",
        "    def _make_layer(self, in_channels, out_channels, num_blocks, stride):\n",
        "        \"\"\"\n",
        "        创建一个残差层\n",
        "        :param in_channels: 输入通道数\n",
        "        :param out_channels: 输出通道数\n",
        "        :param num_blocks: 残差块数量\n",
        "        :param stride: 步长\n",
        "        :return: 残差层\n",
        "        \"\"\"\n",
        "        layers = []\n",
        "        # 第一个残差块可能需要调整通道数和特征图大小\n",
        "        layers.append(ResidualBlock(in_channels, out_channels, stride))\n",
        "\n",
        "        # 后续残差块的输入通道数已经是out_channels\n",
        "        for _ in range(1, num_blocks):\n",
        "            layers.append(ResidualBlock(out_channels, out_channels))\n",
        "\n",
        "        return nn.Sequential(*layers) #*解包\n",
        "\n",
        "    def forward(self, x):\n",
        "        # print(f\"输入形状: {x.shape}\")\n",
        "\n",
        "        x = self.conv1(x)\n",
        "        # print(f\"conv1后形状: {x.shape}\")\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "\n",
        "        x = self.layer1(x)\n",
        "        # print(f\"layer1后形状: {x.shape}\")\n",
        "        x = self.layer2(x) #输出shape是[1, 128, 16, 16]\n",
        "        # print(f\"layer2后形状: {x.shape}\")\n",
        "        x = self.layer3(x) #输出shape是[1, 256, 8, 8]\n",
        "        # print(f\"layer3后形状: {x.shape}\")\n",
        "        x = self.layer4(x) #输出shape是[1, 512, 4, 4]\n",
        "        # print(f\"layer4后形状: {x.shape}\")\n",
        "\n",
        "        x = self.avgpool(x)\n",
        "        # print(f\"avgpool后形状: {x.shape}\")\n",
        "        x = torch.flatten(x, 1)\n",
        "        # print(f\"flatten后形状: {x.shape}\")\n",
        "        x = self.fc(x)\n",
        "        # print(f\"fc后形状: {x.shape}\")\n",
        "\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.185031Z",
          "start_time": "2025-06-26T01:43:33.152657Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mej-sCPF5NcT",
        "outputId": "27b50550-0055-4b27-f76f-31f6b8bfea5e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "批次图像形状: torch.Size([64, 3, 32, 32])\n",
            "批次标签形状: torch.Size([64])\n",
            "----------------------------------------------------------------------------------------------------\n",
            "torch.Size([64, 10])\n"
          ]
        }
      ],
      "source": [
        "# 实例化模型\n",
        "model = ResNet18()\n",
        "\n",
        "# 从train_loader获取第一个批次的数据\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "# 查看批次数据的形状\n",
        "print(\"批次图像形状:\", images.shape)\n",
        "print(\"批次标签形状:\", labels.shape)\n",
        "\n",
        "\n",
        "print('-'*100)\n",
        "# 进行前向传播\n",
        "with torch.no_grad():  # 不需要计算梯度\n",
        "    outputs = model(images)\n",
        "\n",
        "\n",
        "print(outputs.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.203053Z",
          "start_time": "2025-06-26T01:43:33.199532Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LA1oEhZg5NcT",
        "outputId": "7089c80e-387a-47f2-fbac-7d94bb9215ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "需要求梯度的参数总量: 11173962\n",
            "模型总参数量: 11173962\n",
            "\n",
            "各层参数量明细:\n",
            "conv1.weight: 1728 参数\n",
            "bn1.weight: 64 参数\n",
            "bn1.bias: 64 参数\n",
            "layer1.0.conv1.weight: 36864 参数\n",
            "layer1.0.bn1.weight: 64 参数\n",
            "layer1.0.bn1.bias: 64 参数\n",
            "layer1.0.conv2.weight: 36864 参数\n",
            "layer1.0.bn2.weight: 64 参数\n",
            "layer1.0.bn2.bias: 64 参数\n",
            "layer1.1.conv1.weight: 36864 参数\n",
            "layer1.1.bn1.weight: 64 参数\n",
            "layer1.1.bn1.bias: 64 参数\n",
            "layer1.1.conv2.weight: 36864 参数\n",
            "layer1.1.bn2.weight: 64 参数\n",
            "layer1.1.bn2.bias: 64 参数\n",
            "layer2.0.conv1.weight: 73728 参数\n",
            "layer2.0.bn1.weight: 128 参数\n",
            "layer2.0.bn1.bias: 128 参数\n",
            "layer2.0.conv2.weight: 147456 参数\n",
            "layer2.0.bn2.weight: 128 参数\n",
            "layer2.0.bn2.bias: 128 参数\n",
            "layer2.0.shortcut.0.weight: 8192 参数\n",
            "layer2.0.shortcut.1.weight: 128 参数\n",
            "layer2.0.shortcut.1.bias: 128 参数\n",
            "layer2.1.conv1.weight: 147456 参数\n",
            "layer2.1.bn1.weight: 128 参数\n",
            "layer2.1.bn1.bias: 128 参数\n",
            "layer2.1.conv2.weight: 147456 参数\n",
            "layer2.1.bn2.weight: 128 参数\n",
            "layer2.1.bn2.bias: 128 参数\n",
            "layer3.0.conv1.weight: 294912 参数\n",
            "layer3.0.bn1.weight: 256 参数\n",
            "layer3.0.bn1.bias: 256 参数\n",
            "layer3.0.conv2.weight: 589824 参数\n",
            "layer3.0.bn2.weight: 256 参数\n",
            "layer3.0.bn2.bias: 256 参数\n",
            "layer3.0.shortcut.0.weight: 32768 参数\n",
            "layer3.0.shortcut.1.weight: 256 参数\n",
            "layer3.0.shortcut.1.bias: 256 参数\n",
            "layer3.1.conv1.weight: 589824 参数\n",
            "layer3.1.bn1.weight: 256 参数\n",
            "layer3.1.bn1.bias: 256 参数\n",
            "layer3.1.conv2.weight: 589824 参数\n",
            "layer3.1.bn2.weight: 256 参数\n",
            "layer3.1.bn2.bias: 256 参数\n",
            "layer4.0.conv1.weight: 1179648 参数\n",
            "layer4.0.bn1.weight: 512 参数\n",
            "layer4.0.bn1.bias: 512 参数\n",
            "layer4.0.conv2.weight: 2359296 参数\n",
            "layer4.0.bn2.weight: 512 参数\n",
            "layer4.0.bn2.bias: 512 参数\n",
            "layer4.0.shortcut.0.weight: 131072 参数\n",
            "layer4.0.shortcut.1.weight: 512 参数\n",
            "layer4.0.shortcut.1.bias: 512 参数\n",
            "layer4.1.conv1.weight: 2359296 参数\n",
            "layer4.1.bn1.weight: 512 参数\n",
            "layer4.1.bn1.bias: 512 参数\n",
            "layer4.1.conv2.weight: 2359296 参数\n",
            "layer4.1.bn2.weight: 512 参数\n",
            "layer4.1.bn2.bias: 512 参数\n",
            "fc.weight: 5120 参数\n",
            "fc.bias: 10 参数\n"
          ]
        }
      ],
      "source": [
        "# 统计需要求梯度的参数总量\n",
        "total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "print(f\"需要求梯度的参数总量: {total_params}\")\n",
        "\n",
        "# 统计所有参数总量\n",
        "all_params = sum(p.numel() for p in model.parameters())\n",
        "print(f\"模型总参数量: {all_params}\")\n",
        "\n",
        "# 查看每层参数量明细\n",
        "print(\"\\n各层参数量明细:\")\n",
        "for name, param in model.named_parameters():\n",
        "    print(f\"{name}: {param.numel()} 参数\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLyQq0oe5NcT",
        "outputId": "cd2df931-2b98-4451-92ba-bb9672b48c8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "294912"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "128*3*3*256"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GaTC1a9f5NcU"
      },
      "source": [
        "# 各层参数量明细:\n",
        "conv1.weight: 288 参数 3*3*1*32\n",
        "conv1.bias: 32 参数\n",
        "conv2.weight: 9216 参数 3*3*32*32\n",
        "conv2.bias: 32 参数  \n",
        "conv3.weight: 18432 参数 3*3*32*64\n",
        "conv3.bias: 64 参数\n",
        "conv4.weight: 36864 参数  3*3*64*64\n",
        "conv4.bias: 64 参数\n",
        "conv5.weight: 73728 参数\n",
        "conv5.bias: 128 参数\n",
        "conv6.weight: 147456 参数\n",
        "conv6.bias: 128 参数\n",
        "fc1.weight: 294912 参数 128*3*3*256\n",
        "fc1.bias: 256 参数\n",
        "fc2.weight: 2560 参数\n",
        "fc2.bias: 10 参数"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:33.217395Z",
          "start_time": "2025-06-26T01:43:33.203561Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Q1mhn1Q5NcU",
        "outputId": "ed1a703a-7e24-4230-8c4b-c40299838eec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "OrderedDict([('conv1.weight',\n",
              "              tensor([[[[-0.1641,  0.0291, -0.0697],\n",
              "                        [-0.0248,  0.1396, -0.0619],\n",
              "                        [ 0.1538,  0.0203,  0.1647]],\n",
              "              \n",
              "                       [[-0.1151,  0.1655, -0.1396],\n",
              "                        [ 0.0624, -0.1676,  0.0077],\n",
              "                        [ 0.1542, -0.1101, -0.1543]],\n",
              "              \n",
              "                       [[-0.1309,  0.0633,  0.0724],\n",
              "                        [ 0.1430, -0.1514, -0.1432],\n",
              "                        [-0.1838,  0.0982,  0.0166]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0705,  0.0134, -0.1200],\n",
              "                        [ 0.0133, -0.0476, -0.1038],\n",
              "                        [ 0.1833, -0.1922, -0.1526]],\n",
              "              \n",
              "                       [[-0.0944,  0.0584,  0.1359],\n",
              "                        [-0.1287,  0.1488,  0.1668],\n",
              "                        [ 0.1088, -0.1505, -0.0070]],\n",
              "              \n",
              "                       [[ 0.0301, -0.1416,  0.1517],\n",
              "                        [-0.1348,  0.0984, -0.0757],\n",
              "                        [ 0.1662, -0.0930, -0.1898]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.1199, -0.1321, -0.1576],\n",
              "                        [-0.1336,  0.0055, -0.0421],\n",
              "                        [ 0.0801, -0.1807,  0.0560]],\n",
              "              \n",
              "                       [[-0.1682, -0.1275,  0.0050],\n",
              "                        [ 0.1361, -0.0176,  0.1015],\n",
              "                        [-0.1251, -0.1422, -0.0949]],\n",
              "              \n",
              "                       [[-0.1379,  0.0113,  0.0255],\n",
              "                        [ 0.1571,  0.0379,  0.1060],\n",
              "                        [ 0.0474, -0.1898, -0.0342]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-0.0325, -0.1762, -0.0761],\n",
              "                        [ 0.1782, -0.1910, -0.0888],\n",
              "                        [ 0.0033,  0.0862, -0.0433]],\n",
              "              \n",
              "                       [[-0.0916,  0.0112,  0.0833],\n",
              "                        [ 0.0442, -0.0685, -0.0749],\n",
              "                        [-0.0043,  0.0161,  0.1011]],\n",
              "              \n",
              "                       [[ 0.1473,  0.1631,  0.0462],\n",
              "                        [ 0.0837,  0.1111, -0.0167],\n",
              "                        [-0.0338, -0.0787, -0.0090]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0369, -0.1120,  0.0325],\n",
              "                        [-0.1577, -0.0336, -0.0583],\n",
              "                        [ 0.0069,  0.1769, -0.0305]],\n",
              "              \n",
              "                       [[-0.1685, -0.1597,  0.1392],\n",
              "                        [ 0.0935,  0.0447, -0.0921],\n",
              "                        [ 0.1360,  0.0560,  0.0371]],\n",
              "              \n",
              "                       [[-0.1630, -0.0859,  0.1546],\n",
              "                        [-0.0251, -0.0377, -0.0878],\n",
              "                        [-0.0166,  0.0507, -0.1788]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0286, -0.0659,  0.0541],\n",
              "                        [ 0.0144, -0.1594,  0.1500],\n",
              "                        [-0.1642,  0.1081, -0.1572]],\n",
              "              \n",
              "                       [[-0.1488,  0.0940,  0.1585],\n",
              "                        [ 0.0829,  0.0516,  0.0979],\n",
              "                        [-0.1887, -0.0994, -0.1915]],\n",
              "              \n",
              "                       [[ 0.0645,  0.1529, -0.0386],\n",
              "                        [ 0.0057,  0.1042,  0.0298],\n",
              "                        [-0.1462, -0.0659, -0.0654]]]])),\n",
              "             ('bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('bn1.bias',\n",
              "              tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "                      0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "                      0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])),\n",
              "             ('bn1.running_mean',\n",
              "              tensor([ 0.0085,  0.0050,  0.0152,  0.0078, -0.0068, -0.0024,  0.0012, -0.0033,\n",
              "                      -0.0048, -0.0104,  0.0045, -0.0039,  0.0020,  0.0035,  0.0161, -0.0178,\n",
              "                      -0.0061, -0.0228, -0.0046,  0.0005, -0.0145,  0.0085,  0.0048, -0.0043,\n",
              "                       0.0247,  0.0103, -0.0085, -0.0087,  0.0068,  0.0003,  0.0002,  0.0067,\n",
              "                      -0.0036, -0.0022,  0.0197, -0.0047,  0.0281,  0.0224, -0.0166, -0.0074,\n",
              "                       0.0153, -0.0019,  0.0007,  0.0063,  0.0095, -0.0075, -0.0092, -0.0163,\n",
              "                       0.0077,  0.0155,  0.0085,  0.0020,  0.0087, -0.0175,  0.0144, -0.0075,\n",
              "                       0.0043,  0.0106, -0.0133, -0.0035, -0.0035, -0.0066,  0.0107,  0.0036])),\n",
              "             ('bn1.running_var',\n",
              "              tensor([0.9180, 0.9175, 1.0079, 0.9560, 0.9225, 0.9238, 0.9229, 0.9187, 0.9075,\n",
              "                      0.9331, 0.9278, 0.9219, 0.9173, 0.9104, 0.9905, 0.9791, 0.9227, 1.0129,\n",
              "                      0.9334, 0.9107, 0.9701, 0.9297, 0.9077, 0.9128, 1.0861, 0.9181, 0.9362,\n",
              "                      0.9132, 0.9394, 0.9155, 0.9242, 0.9221, 0.9330, 0.9071, 1.0524, 0.9153,\n",
              "                      1.1266, 0.9878, 0.9914, 0.9147, 0.9615, 0.9074, 0.9151, 0.9385, 0.9467,\n",
              "                      0.9203, 0.9408, 0.9521, 0.9178, 0.9770, 0.9157, 0.9090, 0.9562, 0.9518,\n",
              "                      0.9960, 0.9121, 0.9154, 0.9237, 0.9462, 0.9067, 0.9054, 0.9102, 0.9305,\n",
              "                      0.9289])),\n",
              "             ('bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.0.conv1.weight',\n",
              "              tensor([[[[-0.0036, -0.0133, -0.0208],\n",
              "                        [ 0.0300,  0.0175, -0.0295],\n",
              "                        [-0.0315,  0.0099, -0.0234]],\n",
              "              \n",
              "                       [[ 0.0110, -0.0144, -0.0213],\n",
              "                        [ 0.0407,  0.0223,  0.0261],\n",
              "                        [ 0.0187,  0.0148, -0.0172]],\n",
              "              \n",
              "                       [[ 0.0093, -0.0136,  0.0173],\n",
              "                        [ 0.0195, -0.0215, -0.0248],\n",
              "                        [ 0.0320, -0.0284,  0.0144]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 0.0246,  0.0142,  0.0231],\n",
              "                        [-0.0161, -0.0281,  0.0216],\n",
              "                        [ 0.0186, -0.0293, -0.0213]],\n",
              "              \n",
              "                       [[-0.0309,  0.0104, -0.0097],\n",
              "                        [-0.0080, -0.0191, -0.0387],\n",
              "                        [ 0.0160, -0.0127, -0.0391]],\n",
              "              \n",
              "                       [[-0.0260, -0.0312,  0.0165],\n",
              "                        [-0.0235,  0.0270, -0.0305],\n",
              "                        [-0.0011,  0.0011, -0.0169]]],\n",
              "              \n",
              "              \n",
              "                      [[[-0.0258,  0.0196, -0.0081],\n",
              "                        [-0.0128, -0.0199, -0.0153],\n",
              "                        [ 0.0286, -0.0302,  0.0120]],\n",
              "              \n",
              "                       [[-0.0310,  0.0103, -0.0416],\n",
              "                        [ 0.0381,  0.0298, -0.0393],\n",
              "                        [ 0.0050,  0.0159, -0.0179]],\n",
              "              \n",
              "                       [[-0.0153, -0.0329,  0.0258],\n",
              "                        [-0.0048,  0.0179, -0.0358],\n",
              "                        [ 0.0014, -0.0313,  0.0198]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0275, -0.0308, -0.0051],\n",
              "                        [ 0.0193, -0.0295,  0.0175],\n",
              "                        [ 0.0081, -0.0410,  0.0016]],\n",
              "              \n",
              "                       [[-0.0146,  0.0297,  0.0377],\n",
              "                        [ 0.0081,  0.0136, -0.0346],\n",
              "                        [ 0.0377, -0.0163,  0.0118]],\n",
              "              \n",
              "                       [[-0.0349, -0.0403, -0.0139],\n",
              "                        [ 0.0350,  0.0087,  0.0136],\n",
              "                        [-0.0144, -0.0398,  0.0200]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0187,  0.0217, -0.0271],\n",
              "                        [ 0.0336, -0.0311, -0.0253],\n",
              "                        [ 0.0222,  0.0212, -0.0320]],\n",
              "              \n",
              "                       [[-0.0269,  0.0360,  0.0324],\n",
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              "                        [-0.0085, -0.0001,  0.0378]],\n",
              "              \n",
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              "                        [-0.0310,  0.0272,  0.0169],\n",
              "                        [-0.0123,  0.0409,  0.0144]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0412, -0.0408,  0.0349],\n",
              "                        [-0.0038, -0.0257, -0.0219],\n",
              "                        [ 0.0123, -0.0209,  0.0008]],\n",
              "              \n",
              "                       [[-0.0189,  0.0387,  0.0061],\n",
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              "                        [-0.0392,  0.0409,  0.0283]],\n",
              "              \n",
              "                       [[ 0.0107,  0.0282, -0.0085],\n",
              "                        [ 0.0021, -0.0275,  0.0414],\n",
              "                        [-0.0228, -0.0350, -0.0400]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 0.0315, -0.0146, -0.0384],\n",
              "                        [-0.0157,  0.0357, -0.0260],\n",
              "                        [ 0.0266, -0.0173, -0.0243]],\n",
              "              \n",
              "                       [[-0.0305,  0.0075,  0.0378],\n",
              "                        [ 0.0222,  0.0263,  0.0153],\n",
              "                        [-0.0353, -0.0105, -0.0351]],\n",
              "              \n",
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              "                        [-0.0174, -0.0381, -0.0355],\n",
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              "              \n",
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              "                        [ 0.0186,  0.0241, -0.0036],\n",
              "                        [-0.0160, -0.0082,  0.0232]],\n",
              "              \n",
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              "                        [-0.0304, -0.0110, -0.0006],\n",
              "                        [-0.0163, -0.0047, -0.0347]],\n",
              "              \n",
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              "                        [-0.0240, -0.0414,  0.0212],\n",
              "                        [ 0.0369,  0.0416,  0.0096]]],\n",
              "              \n",
              "              \n",
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              "                        [ 0.0290,  0.0054,  0.0039],\n",
              "                        [-0.0226,  0.0344, -0.0391]],\n",
              "              \n",
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              "              \n",
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              "                        [-0.0210, -0.0121,  0.0173],\n",
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              "              \n",
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              "              \n",
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              "                        [ 0.0097, -0.0039,  0.0117],\n",
              "                        [ 0.0029,  0.0277,  0.0046]],\n",
              "              \n",
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              "                        [-0.0413, -0.0302, -0.0374],\n",
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              "              \n",
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              "              \n",
              "              \n",
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              "                        [-0.0128, -0.0252, -0.0087],\n",
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              "              \n",
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              "                        [ 0.0279,  0.0038,  0.0187]],\n",
              "              \n",
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              "                        [ 0.0175, -0.0206,  0.0284]],\n",
              "              \n",
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              "                        [ 0.0022,  0.0327, -0.0239],\n",
              "                        [ 0.0062,  0.0071, -0.0129]],\n",
              "              \n",
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              "              \n",
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              "                        [ 0.0255,  0.0175, -0.0195],\n",
              "                        [-0.0364, -0.0364,  0.0009]]]])),\n",
              "             ('layer1.0.bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer1.0.bn1.bias',\n",
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              "             ('layer1.0.bn1.running_mean',\n",
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              "                      -0.0235,  0.0241,  0.0244,  0.0020,  0.0054, -0.0158, -0.0340, -0.0049])),\n",
              "             ('layer1.0.bn1.running_var',\n",
              "              tensor([0.9099, 0.9181, 0.9102, 0.9107, 0.9087, 0.9126, 0.9179, 0.9129, 0.9071,\n",
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              "                      0.9104, 0.9056, 0.9063, 0.9078, 0.9068, 0.9066, 0.9126, 0.9059, 0.9073,\n",
              "                      0.9071])),\n",
              "             ('layer1.0.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.0.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 0.0385, -0.0128, -0.0026]]],\n",
              "              \n",
              "              \n",
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              "                        [ 0.0409,  0.0312, -0.0380]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0087,  0.0023,  0.0309]]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0136,  0.0291, -0.0064]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 0.0212, -0.0026, -0.0291],\n",
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              "              \n",
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              "                        [-0.0130,  0.0349,  0.0281]],\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-0.0275,  0.0181, -0.0096]]]])),\n",
              "             ('layer1.0.bn2.weight',\n",
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              "             ('layer1.0.bn2.bias',\n",
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              "                       2.7037e-02,  1.4559e-02, -3.4357e-02, -3.0507e-03])),\n",
              "             ('layer1.0.bn2.running_var',\n",
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              "                      0.9104])),\n",
              "             ('layer1.0.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.1.conv1.weight',\n",
              "              tensor([[[[-1.4022e-02,  1.5670e-03,  4.1040e-02],\n",
              "                        [ 2.7990e-02, -1.4963e-02,  4.1090e-02],\n",
              "                        [ 3.8161e-02,  4.0672e-02,  3.4640e-02]],\n",
              "              \n",
              "                       [[ 4.1386e-02,  1.2851e-03,  1.1833e-02],\n",
              "                        [-2.3785e-02,  2.2180e-02,  3.7074e-02],\n",
              "                        [-8.3258e-03,  2.5588e-02, -3.6927e-02]],\n",
              "              \n",
              "                       [[-3.3811e-02, -3.7303e-02,  2.5303e-02],\n",
              "                        [ 2.4874e-02, -1.3450e-02, -1.1548e-02],\n",
              "                        [ 3.0976e-02, -3.3615e-02, -2.9441e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.5283e-02,  2.7519e-02,  3.4797e-02],\n",
              "                        [-4.6434e-03, -2.3723e-02,  1.1395e-02],\n",
              "                        [ 2.7391e-02,  2.0536e-03, -2.2889e-02]],\n",
              "              \n",
              "                       [[ 3.2035e-02, -1.4192e-04,  4.3113e-04],\n",
              "                        [-4.0580e-02, -2.7419e-02, -3.1718e-02],\n",
              "                        [-1.8912e-02, -1.7544e-02,  1.0076e-02]],\n",
              "              \n",
              "                       [[ 3.8696e-02,  1.2472e-05,  1.2114e-02],\n",
              "                        [ 5.8819e-03, -3.8489e-02, -3.2663e-02],\n",
              "                        [ 1.2843e-02, -1.9095e-02,  2.0217e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-8.9160e-03,  1.2738e-02,  2.9455e-03],\n",
              "                        [ 3.8859e-02, -3.5577e-02, -3.6449e-02],\n",
              "                        [ 3.6197e-02, -3.4455e-02, -2.3170e-02]],\n",
              "              \n",
              "                       [[ 2.7528e-02, -3.8227e-02, -1.9742e-02],\n",
              "                        [ 3.9282e-02,  3.9266e-02, -3.7809e-02],\n",
              "                        [-3.3843e-02, -3.0113e-02, -3.0896e-02]],\n",
              "              \n",
              "                       [[ 3.8524e-02, -1.6047e-02,  4.1623e-02],\n",
              "                        [ 2.4554e-02,  4.1238e-03, -1.4992e-03],\n",
              "                        [-1.5906e-02,  1.7821e-02,  3.5266e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.3911e-02, -9.0997e-03, -2.0765e-02],\n",
              "                        [ 8.4409e-03, -1.9208e-02,  7.8827e-04],\n",
              "                        [ 2.9654e-02, -3.8339e-03, -2.3822e-02]],\n",
              "              \n",
              "                       [[-1.6714e-02,  3.7593e-02, -3.6961e-02],\n",
              "                        [ 7.5391e-03, -3.3554e-02,  3.7702e-03],\n",
              "                        [ 3.7771e-02,  2.4491e-02,  3.9386e-02]],\n",
              "              \n",
              "                       [[-3.8801e-02, -1.2270e-03,  3.1855e-02],\n",
              "                        [-3.2662e-02,  1.6948e-02, -2.7566e-02],\n",
              "                        [ 7.0883e-03,  4.0824e-02, -2.2050e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.7369e-02, -2.7541e-02, -3.9344e-02],\n",
              "                        [-3.9705e-02, -3.8211e-02,  2.7477e-02],\n",
              "                        [-3.5965e-02,  3.6379e-02,  2.4551e-02]],\n",
              "              \n",
              "                       [[ 2.5489e-02, -3.2896e-02,  1.4675e-02],\n",
              "                        [ 1.8327e-02,  3.4872e-02,  2.5055e-02],\n",
              "                        [-4.1427e-02, -1.0478e-02, -4.0328e-02]],\n",
              "              \n",
              "                       [[-1.7680e-02, -1.6454e-02,  2.6503e-02],\n",
              "                        [ 1.3308e-02,  2.6108e-02, -3.1472e-02],\n",
              "                        [ 1.3742e-04, -1.9142e-04, -3.8397e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.2235e-02,  2.5989e-02,  9.4723e-03],\n",
              "                        [-9.1573e-03, -3.5956e-02,  1.7936e-02],\n",
              "                        [-3.5238e-02, -1.6024e-02,  3.1139e-02]],\n",
              "              \n",
              "                       [[ 3.0654e-02,  2.1588e-02,  1.4355e-02],\n",
              "                        [-8.9810e-03, -1.6782e-02, -3.5494e-02],\n",
              "                        [ 1.8023e-02,  8.9971e-03, -1.0439e-02]],\n",
              "              \n",
              "                       [[ 2.3471e-02,  1.2387e-02, -3.5869e-03],\n",
              "                        [-1.2368e-02,  3.0456e-02,  3.4151e-02],\n",
              "                        [-2.2679e-02, -3.5452e-02, -1.6662e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 1.0855e-02,  1.3269e-02, -8.0225e-03],\n",
              "                        [-1.4975e-03, -1.6172e-02,  3.0579e-02],\n",
              "                        [ 1.9891e-02, -8.4590e-03, -1.7339e-02]],\n",
              "              \n",
              "                       [[ 2.0701e-02,  3.7416e-02,  4.0419e-02],\n",
              "                        [-3.2957e-04, -1.4565e-02,  2.0413e-03],\n",
              "                        [-3.3324e-02, -1.9723e-02,  6.7615e-03]],\n",
              "              \n",
              "                       [[-8.4332e-03,  1.4666e-02, -2.8895e-02],\n",
              "                        [ 2.5356e-02,  2.2754e-02,  4.0690e-02],\n",
              "                        [ 1.6671e-02, -3.3839e-02,  2.5079e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.9958e-02, -3.9282e-02,  2.0908e-02],\n",
              "                        [ 3.8680e-03,  3.1627e-02, -3.5898e-02],\n",
              "                        [-1.9732e-02, -8.4246e-03,  2.7933e-02]],\n",
              "              \n",
              "                       [[-2.0437e-02,  1.9914e-02, -1.2384e-02],\n",
              "                        [ 4.9096e-03, -2.0353e-02,  8.5127e-03],\n",
              "                        [ 1.5981e-02, -1.7646e-02, -3.6678e-03]],\n",
              "              \n",
              "                       [[-3.5779e-02, -8.1803e-03,  2.4848e-02],\n",
              "                        [-3.1548e-02, -2.5561e-02, -2.3870e-02],\n",
              "                        [-2.6670e-02, -2.7673e-02,  2.2927e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-3.1933e-03, -2.1477e-02,  1.2815e-02],\n",
              "                        [-1.2716e-02, -4.3349e-03,  3.8199e-02],\n",
              "                        [ 1.0015e-02,  1.2215e-02, -4.0180e-02]],\n",
              "              \n",
              "                       [[ 2.8657e-02,  2.6729e-02,  2.8972e-02],\n",
              "                        [ 2.0875e-02, -2.2138e-02,  1.3403e-02],\n",
              "                        [-3.2188e-02,  3.7650e-02,  2.9869e-02]],\n",
              "              \n",
              "                       [[ 2.9426e-02,  4.0979e-02,  2.1728e-02],\n",
              "                        [-7.5128e-03, -1.4830e-02,  1.2338e-02],\n",
              "                        [ 5.4507e-03, -1.8035e-03, -5.4716e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.9762e-02,  1.1299e-02, -1.2909e-02],\n",
              "                        [ 1.9214e-02,  1.2596e-02,  2.1646e-02],\n",
              "                        [-2.6637e-02, -1.5085e-02,  1.3249e-02]],\n",
              "              \n",
              "                       [[ 1.3025e-02, -3.0491e-02, -2.5254e-02],\n",
              "                        [-2.1851e-02,  3.8601e-02, -2.5562e-02],\n",
              "                        [ 1.9019e-02, -3.3250e-02, -2.2785e-02]],\n",
              "              \n",
              "                       [[ 1.0211e-02, -3.9861e-02,  3.6622e-02],\n",
              "                        [ 4.7193e-03,  6.1421e-03,  1.8421e-02],\n",
              "                        [-2.4678e-02,  2.5763e-02, -9.9964e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.5444e-02, -6.9709e-03,  2.8209e-03],\n",
              "                        [ 1.6803e-02,  1.2257e-02, -8.3690e-03],\n",
              "                        [-1.0968e-03, -1.7370e-02, -3.9779e-02]],\n",
              "              \n",
              "                       [[-1.5152e-02,  8.9839e-03,  3.1572e-02],\n",
              "                        [ 2.4729e-02, -3.3842e-02, -7.7756e-03],\n",
              "                        [-2.8540e-02,  3.0496e-02, -2.7631e-03]],\n",
              "              \n",
              "                       [[-1.9606e-02,  2.5947e-02,  1.2795e-02],\n",
              "                        [-1.1563e-02,  2.2604e-02,  2.0241e-02],\n",
              "                        [ 2.6178e-02, -1.7178e-02,  2.0409e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 6.0009e-03, -1.5239e-02,  3.9641e-02],\n",
              "                        [-6.1857e-04, -9.5992e-03,  1.0957e-03],\n",
              "                        [-2.8040e-02, -1.2803e-02, -3.5250e-02]],\n",
              "              \n",
              "                       [[ 3.2768e-02, -1.2112e-02,  2.1724e-02],\n",
              "                        [-2.9036e-02, -3.9660e-02,  4.0147e-02],\n",
              "                        [ 1.5113e-02, -1.1622e-02,  2.5326e-02]],\n",
              "              \n",
              "                       [[ 2.1681e-02,  3.7297e-02, -2.7407e-02],\n",
              "                        [ 7.7968e-03,  1.5730e-02,  3.6105e-02],\n",
              "                        [ 5.8899e-03,  2.9516e-02,  3.8372e-03]]]])),\n",
              "             ('layer1.1.bn1.weight',\n",
              "              tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
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              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
              "                      1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])),\n",
              "             ('layer1.1.bn1.bias',\n",
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              "             ('layer1.1.bn1.running_mean',\n",
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              "                      -0.0300,  0.0426, -0.0518, -0.0121, -0.0150, -0.0331, -0.0523, -0.0555,\n",
              "                       0.0298, -0.0127,  0.0306,  0.0703,  0.0346, -0.0073,  0.0256,  0.0329,\n",
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              "                       0.0236,  0.0065, -0.0021, -0.0773,  0.0469, -0.0393,  0.0192,  0.0391,\n",
              "                      -0.0556, -0.0096,  0.0189,  0.0113, -0.0422,  0.0134, -0.0461,  0.0206,\n",
              "                      -0.0532, -0.0437,  0.0690,  0.0038,  0.0074,  0.0672,  0.0009, -0.0007,\n",
              "                      -0.0372,  0.0038, -0.0613,  0.0245,  0.0486, -0.0016, -0.0042,  0.0136])),\n",
              "             ('layer1.1.bn1.running_var',\n",
              "              tensor([0.9146, 0.9114, 0.9326, 0.9228, 0.9234, 0.9296, 0.9163, 0.9162, 0.9576,\n",
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              "                      0.9225, 0.9164, 0.9278, 0.9144, 0.9218, 0.9183, 0.9214, 0.9159, 0.9393,\n",
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              "                      0.9263, 0.9177, 0.9266, 0.9311, 0.9283, 0.9295, 0.9176, 0.9371, 0.9205,\n",
              "                      0.9181])),\n",
              "             ('layer1.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer1.1.conv2.weight',\n",
              "              tensor([[[[ 3.3237e-02,  1.5538e-02, -3.5369e-03],\n",
              "                        [-4.0774e-02, -1.5009e-02, -4.0551e-02],\n",
              "                        [-3.0999e-02,  2.8759e-02,  2.7978e-02]],\n",
              "              \n",
              "                       [[-8.4059e-03, -2.3024e-02, -4.0574e-02],\n",
              "                        [ 3.4803e-02,  3.9049e-02,  2.7516e-03],\n",
              "                        [-3.4215e-02, -5.4276e-03,  1.6434e-02]],\n",
              "              \n",
              "                       [[ 2.5984e-02, -2.5605e-02,  7.0540e-03],\n",
              "                        [-1.0577e-02, -1.6257e-03, -2.8401e-02],\n",
              "                        [ 2.3108e-02, -1.3837e-02,  1.1607e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.2106e-02,  4.0896e-02,  9.5420e-03],\n",
              "                        [ 1.4787e-02, -6.8296e-04, -1.6827e-02],\n",
              "                        [ 1.4853e-02,  7.6250e-03, -2.2574e-03]],\n",
              "              \n",
              "                       [[-7.8986e-03,  1.1173e-02, -4.1080e-02],\n",
              "                        [-2.6439e-02,  3.6721e-02, -3.5399e-02],\n",
              "                        [ 1.4828e-02,  2.5474e-02,  3.6238e-02]],\n",
              "              \n",
              "                       [[ 1.6182e-02, -8.5220e-03, -3.4476e-02],\n",
              "                        [ 3.7233e-02, -3.0254e-02,  7.1001e-03],\n",
              "                        [ 4.1510e-02, -7.0815e-03,  3.4571e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 6.4047e-03,  3.6286e-03, -1.1392e-02],\n",
              "                        [ 5.0244e-03,  2.3792e-02, -5.2553e-03],\n",
              "                        [-2.5730e-02,  1.2037e-02, -3.4522e-02]],\n",
              "              \n",
              "                       [[-2.5197e-02, -5.8766e-03, -7.2432e-03],\n",
              "                        [-1.5846e-02, -1.9605e-02, -1.3372e-02],\n",
              "                        [ 3.4289e-02, -2.4454e-02, -2.6651e-02]],\n",
              "              \n",
              "                       [[-3.4751e-02,  6.2281e-03,  1.6697e-02],\n",
              "                        [ 1.6619e-02,  1.0184e-02, -7.3288e-03],\n",
              "                        [-1.1555e-02, -1.4759e-02,  2.4652e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 3.2301e-02, -1.9370e-03,  1.0009e-02],\n",
              "                        [-2.2870e-02,  1.9612e-02,  7.3356e-03],\n",
              "                        [ 1.9046e-02, -1.0673e-02,  1.6012e-02]],\n",
              "              \n",
              "                       [[-1.4530e-02,  4.1331e-03,  2.5893e-02],\n",
              "                        [-3.3361e-02,  1.9795e-02, -8.2314e-03],\n",
              "                        [-9.4248e-03, -2.8933e-02, -2.0890e-02]],\n",
              "              \n",
              "                       [[ 9.2819e-03,  3.7085e-02, -2.6768e-02],\n",
              "                        [ 2.9530e-02,  1.8241e-02, -3.3062e-02],\n",
              "                        [ 2.8276e-02, -2.7853e-02, -2.8843e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 2.8241e-02, -9.9800e-03,  3.3388e-02],\n",
              "                        [ 2.9239e-02, -3.9126e-02, -1.5276e-02],\n",
              "                        [ 5.1719e-03, -1.5624e-02, -3.2920e-02]],\n",
              "              \n",
              "                       [[ 3.1907e-02, -2.6196e-02, -5.1512e-03],\n",
              "                        [-3.2432e-02,  3.9575e-02, -1.1888e-02],\n",
              "                        [-2.4685e-02, -2.8360e-02,  3.8618e-02]],\n",
              "              \n",
              "                       [[-1.7992e-02,  1.7350e-02, -1.7719e-02],\n",
              "                        [ 1.2583e-02, -3.4626e-02,  8.1263e-03],\n",
              "                        [ 2.9974e-02, -3.6321e-03,  2.3903e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.5746e-03,  4.0250e-02, -3.8354e-02],\n",
              "                        [-3.7169e-02, -7.5523e-03, -3.1973e-02],\n",
              "                        [-2.0272e-02, -3.9792e-02,  3.6020e-02]],\n",
              "              \n",
              "                       [[ 2.5417e-02,  2.6879e-02, -2.0195e-03],\n",
              "                        [-3.2147e-02,  3.8716e-02,  2.3149e-02],\n",
              "                        [ 3.3153e-02, -2.8655e-03,  3.5650e-02]],\n",
              "              \n",
              "                       [[-3.3974e-02, -1.1080e-02,  3.3012e-02],\n",
              "                        [-3.4650e-02, -3.6195e-02,  3.6785e-02],\n",
              "                        [-2.4833e-03, -1.6139e-03, -1.5350e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[ 3.8392e-02,  1.9366e-02,  1.3413e-02],\n",
              "                        [-3.4873e-02, -2.0121e-02, -4.1442e-02],\n",
              "                        [-3.8181e-02, -4.0488e-02, -3.1893e-02]],\n",
              "              \n",
              "                       [[ 1.5134e-02,  1.5894e-02,  2.8496e-02],\n",
              "                        [ 4.1221e-02,  6.6920e-03,  1.4717e-02],\n",
              "                        [ 4.1270e-02,  3.0853e-02, -3.7802e-03]],\n",
              "              \n",
              "                       [[ 3.8072e-02,  3.5789e-02, -2.5041e-02],\n",
              "                        [ 2.9241e-02,  1.9217e-02,  2.3740e-02],\n",
              "                        [-3.1041e-02,  1.2535e-02, -1.7827e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-4.0149e-02, -3.3909e-02,  1.2252e-02],\n",
              "                        [-1.3803e-02, -2.5108e-02,  2.2876e-02],\n",
              "                        [ 1.5179e-02,  3.2374e-02,  2.8083e-02]],\n",
              "              \n",
              "                       [[-1.8079e-02,  3.3205e-02, -2.4990e-02],\n",
              "                        [-3.8233e-02, -1.3894e-02,  2.8131e-02],\n",
              "                        [ 2.4217e-02, -2.9243e-02,  1.1358e-02]],\n",
              "              \n",
              "                       [[-2.3996e-02,  4.3007e-03,  2.8230e-02],\n",
              "                        [-7.2131e-03, -2.6535e-03, -1.6066e-02],\n",
              "                        [-3.2289e-02,  3.1647e-03,  3.2345e-03]]],\n",
              "              \n",
              "              \n",
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              "                        [ 2.4332e-02,  2.5848e-03,  1.0209e-03],\n",
              "                        [ 1.5559e-02, -4.0121e-02, -3.3970e-02]],\n",
              "              \n",
              "                       [[ 3.1385e-02,  1.7914e-02, -1.3110e-02],\n",
              "                        [-2.0463e-02, -3.4221e-02,  2.4559e-02],\n",
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              "              \n",
              "                       [[-5.6436e-03, -9.0476e-03,  7.9404e-04],\n",
              "                        [-2.0148e-02,  2.7210e-03, -1.5888e-02],\n",
              "                        [-3.4551e-02,  3.0365e-02,  3.3702e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.1213e-02, -3.7955e-03,  4.0966e-02],\n",
              "                        [-9.8658e-04,  7.0086e-03, -3.7341e-02],\n",
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              "              \n",
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              "              \n",
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              "                        [-4.5205e-05,  1.8838e-02,  3.8891e-02],\n",
              "                        [-3.0257e-03, -4.1509e-04,  1.5526e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-1.2738e-02, -3.5873e-02, -3.0656e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-7.7566e-03,  1.3623e-02,  3.8934e-02],\n",
              "                        [ 2.7144e-02, -1.0232e-02, -3.0688e-02],\n",
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              "              \n",
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              "                        [-3.2141e-02, -3.1341e-02, -5.0508e-03],\n",
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              "              \n",
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              "                        [-1.9349e-02,  9.3473e-03, -2.0277e-03],\n",
              "                        [-3.3679e-02, -4.3327e-03,  1.8043e-02]]]])),\n",
              "             ('layer1.1.bn2.weight',\n",
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              "             ('layer1.1.bn2.bias',\n",
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              "             ('layer1.1.bn2.running_mean',\n",
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              "             ('layer1.1.bn2.running_var',\n",
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              "                      0.9148])),\n",
              "             ('layer1.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer2.0.conv1.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                      0.9315, 0.9369])),\n",
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              "             ('layer2.0.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                      -0.0140,  0.0836,  0.0886,  0.0083, -0.0067,  0.0232,  0.0267, -0.0300])),\n",
              "             ('layer2.0.shortcut.1.running_var',\n",
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              "                      0.9325, 0.9340])),\n",
              "             ('layer2.0.shortcut.1.num_batches_tracked', tensor(1)),\n",
              "             ('layer2.1.conv1.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-1.4849e-02, -1.2507e-02,  2.2758e-03],\n",
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              "              \n",
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              "              \n",
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              "                        [-2.6184e-02,  7.2679e-03, -2.5724e-02]]],\n",
              "              \n",
              "              \n",
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              "                        [-2.4895e-02, -1.5584e-02,  1.5877e-02],\n",
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              "              \n",
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              "                        [-1.6426e-02,  9.7527e-03,  1.9519e-02],\n",
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              "              \n",
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              "                        [-2.2036e-02,  2.3602e-03, -1.2850e-02],\n",
              "                        [ 2.5813e-02,  1.3989e-02,  5.5406e-05]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 5.5035e-03,  1.9614e-02,  2.8961e-02],\n",
              "                        [-1.2246e-02,  2.3208e-02,  3.8972e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "                        [ 1.0347e-02,  5.7239e-04, -2.6069e-02]],\n",
              "              \n",
              "                       [[ 3.2235e-03, -7.8265e-04, -1.7428e-03],\n",
              "                        [ 1.3766e-02,  2.2591e-02, -6.7095e-03],\n",
              "                        [-1.5256e-02,  2.9053e-02, -2.9288e-02]],\n",
              "              \n",
              "                       [[-1.2514e-02,  2.7171e-02,  8.5966e-03],\n",
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              "                        [ 7.5077e-03,  1.6008e-02, -1.5602e-03]],\n",
              "              \n",
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              "              \n",
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              "                        [ 2.8824e-02, -2.0032e-03,  6.6681e-03],\n",
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              "              \n",
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              "              \n",
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              "                        [ 1.8601e-03, -2.6697e-02, -1.1973e-02]]],\n",
              "              \n",
              "              \n",
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              "              \n",
              "              \n",
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              "                        [-5.2204e-03, -1.8352e-02,  4.3102e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 2.5791e-02, -4.2678e-03, -2.8254e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-8.3211e-03, -1.3618e-02,  7.3769e-03],\n",
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              "              \n",
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              "                        [ 1.8070e-02,  1.5559e-02, -1.2686e-02]],\n",
              "              \n",
              "                       [[ 1.5783e-02, -2.5923e-02, -2.0716e-02],\n",
              "                        [ 2.1653e-02, -1.1811e-02,  2.4846e-02],\n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       [[-5.7559e-03, -1.9874e-03, -4.5114e-03],\n",
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              "              \n",
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              "              \n",
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              "                        [ 2.9244e-02, -1.8510e-02,  2.1317e-02]]]])),\n",
              "             ('layer2.1.bn1.weight',\n",
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              "                      0.9190, 0.9183, 0.9182, 0.9156, 0.9171, 0.9176, 0.9292, 0.9195, 0.9223,\n",
              "                      0.9233, 0.9182, 0.9214, 0.9202, 0.9192, 0.9183, 0.9223, 0.9209, 0.9235,\n",
              "                      0.9187, 0.9265, 0.9195, 0.9211, 0.9200, 0.9198, 0.9166, 0.9199, 0.9210,\n",
              "                      0.9181, 0.9183, 0.9195, 0.9199, 0.9193, 0.9194, 0.9237, 0.9168, 0.9202,\n",
              "                      0.9208, 0.9177, 0.9189, 0.9238, 0.9238, 0.9230, 0.9209, 0.9207, 0.9216,\n",
              "                      0.9214, 0.9187, 0.9177, 0.9292, 0.9358, 0.9320, 0.9194, 0.9167, 0.9213,\n",
              "                      0.9182, 0.9163, 0.9193, 0.9238, 0.9167, 0.9244, 0.9239, 0.9192, 0.9204,\n",
              "                      0.9203, 0.9189])),\n",
              "             ('layer2.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer2.1.conv2.weight',\n",
              "              tensor([[[[-5.1886e-04, -2.5355e-02,  1.6943e-02],\n",
              "                        [ 9.0147e-03,  1.4130e-02,  1.8242e-02],\n",
              "                        [-1.1822e-02,  1.1617e-02, -2.5720e-02]],\n",
              "              \n",
              "                       [[-2.1281e-02,  6.3258e-03, -1.2576e-02],\n",
              "                        [-1.2746e-02, -1.7224e-02, -2.6037e-02],\n",
              "                        [ 1.8607e-02, -2.2377e-02,  1.2036e-02]],\n",
              "              \n",
              "                       [[ 2.4526e-02,  2.0143e-02,  2.9020e-02],\n",
              "                        [ 1.2878e-02,  1.2281e-02, -4.2203e-03],\n",
              "                        [ 1.8252e-02, -1.0755e-02,  1.4622e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.0420e-02,  8.8659e-03, -2.6220e-02],\n",
              "                        [ 2.1745e-02, -4.1888e-03, -2.4718e-02],\n",
              "                        [-1.0489e-03,  1.0719e-02,  1.7456e-02]],\n",
              "              \n",
              "                       [[-1.8181e-02,  7.0822e-03,  1.3625e-02],\n",
              "                        [-1.6432e-02,  2.2378e-02, -4.1147e-03],\n",
              "                        [ 2.1916e-02, -3.6717e-03,  4.7497e-03]],\n",
              "              \n",
              "                       [[ 2.7010e-02, -5.6075e-03, -1.6100e-02],\n",
              "                        [-2.0396e-02,  2.0348e-03,  1.9489e-02],\n",
              "                        [-2.8721e-02,  1.5674e-03, -6.9707e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-2.7527e-02,  2.4244e-02, -9.8503e-03],\n",
              "                        [ 2.7297e-02, -2.3586e-02, -2.2318e-02],\n",
              "                        [-2.1321e-02, -1.7629e-02, -1.2137e-02]],\n",
              "              \n",
              "                       [[-2.0256e-02, -1.2357e-02,  2.9200e-02],\n",
              "                        [-2.6376e-02,  6.8558e-03,  2.8560e-02],\n",
              "                        [-2.1619e-02,  2.8740e-02, -8.2953e-03]],\n",
              "              \n",
              "                       [[-1.8966e-02,  8.1477e-03, -3.1017e-03],\n",
              "                        [-2.1834e-02, -1.0674e-02, -2.3187e-02],\n",
              "                        [-1.9405e-02, -2.7313e-02,  4.3152e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.5650e-02, -2.1036e-02,  1.3694e-02],\n",
              "                        [-1.9194e-02, -1.8353e-02, -2.7177e-02],\n",
              "                        [ 1.9330e-02, -2.8451e-02, -5.8603e-03]],\n",
              "              \n",
              "                       [[ 2.5593e-02, -1.2227e-02,  1.7728e-02],\n",
              "                        [ 2.1275e-02,  1.6720e-03, -1.5132e-02],\n",
              "                        [ 3.7133e-03, -1.1929e-02,  2.3310e-02]],\n",
              "              \n",
              "                       [[ 5.7998e-03, -7.1623e-03,  1.6829e-02],\n",
              "                        [-2.4196e-02,  1.3877e-02,  6.7354e-03],\n",
              "                        [-2.6534e-02,  1.1106e-02, -1.8861e-03]]],\n",
              "              \n",
              "              \n",
              "                      [[[-1.9474e-02,  6.8343e-03,  3.7291e-03],\n",
              "                        [-1.7153e-03,  4.3901e-03, -2.6858e-02],\n",
              "                        [ 5.4577e-05, -1.6038e-02, -2.2596e-02]],\n",
              "              \n",
              "                       [[ 3.0584e-03, -2.2946e-02,  2.7228e-02],\n",
              "                        [ 1.7466e-02,  2.4270e-02, -2.7372e-02],\n",
              "                        [ 1.3395e-02,  7.2835e-03,  1.3343e-02]],\n",
              "              \n",
              "                       [[-1.0412e-02, -1.1971e-02,  5.0096e-03],\n",
              "                        [ 2.8153e-02,  4.3329e-03, -6.5228e-03],\n",
              "                        [-7.6120e-04,  5.2304e-03,  9.0848e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-2.3279e-02,  1.6702e-02, -2.2398e-02],\n",
              "                        [ 2.6011e-02,  1.2598e-02,  1.4799e-02],\n",
              "                        [ 9.8350e-03,  1.9088e-02, -2.1707e-02]],\n",
              "              \n",
              "                       [[ 2.1401e-02,  4.6013e-03,  1.2520e-02],\n",
              "                        [ 2.6987e-02,  4.7261e-03,  2.3425e-02],\n",
              "                        [ 1.5371e-02,  2.6142e-02,  2.9135e-03]],\n",
              "              \n",
              "                       [[-2.6670e-02,  1.8422e-02,  5.4952e-03],\n",
              "                        [ 2.8311e-02, -2.8491e-02, -4.9769e-03],\n",
              "                        [-1.7856e-02, -8.8531e-04, -1.9280e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-2.1175e-02,  4.2819e-03,  1.2604e-02],\n",
              "                        [ 6.8998e-03,  9.2401e-03,  1.7188e-02],\n",
              "                        [ 2.8431e-03,  2.9271e-02,  2.4639e-02]],\n",
              "              \n",
              "                       [[-2.8476e-02, -2.1857e-02,  1.9564e-02],\n",
              "                        [ 5.1078e-04,  2.4921e-02, -2.1481e-02],\n",
              "                        [-3.8253e-03, -5.7416e-03, -2.1119e-02]],\n",
              "              \n",
              "                       [[ 2.4072e-02, -2.8013e-02,  2.5329e-02],\n",
              "                        [-2.2420e-02,  2.2130e-02,  1.3399e-02],\n",
              "                        [-6.7870e-03,  6.3031e-03,  1.6948e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-2.0964e-02,  2.8234e-02,  4.3049e-03],\n",
              "                        [ 1.1348e-02,  9.9818e-03, -8.2574e-03],\n",
              "                        [ 1.2032e-02,  2.6721e-02,  1.2991e-02]],\n",
              "              \n",
              "                       [[ 1.1387e-04, -1.7790e-02, -1.2501e-02],\n",
              "                        [-8.6165e-03,  1.5931e-02, -1.5794e-02],\n",
              "                        [ 5.1454e-03,  4.0133e-03,  9.9031e-03]],\n",
              "              \n",
              "                       [[ 1.8134e-02,  1.8549e-03, -2.2970e-02],\n",
              "                        [ 2.7807e-02, -2.4059e-02,  2.1981e-03],\n",
              "                        [ 2.3106e-02, -2.2650e-02,  2.5393e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 3.3962e-04, -2.8326e-02,  2.8904e-02],\n",
              "                        [ 2.3499e-03, -2.7144e-02,  5.0989e-03],\n",
              "                        [ 2.7613e-02,  3.3605e-03,  1.3998e-03]],\n",
              "              \n",
              "                       [[-1.7618e-02, -1.8031e-02, -6.7483e-03],\n",
              "                        [ 1.7021e-02, -2.4678e-02,  9.4921e-03],\n",
              "                        [-1.7689e-02,  1.4953e-02,  2.4275e-02]],\n",
              "              \n",
              "                       [[-2.3738e-02, -2.4789e-02, -1.9821e-02],\n",
              "                        [-1.0751e-02,  1.6039e-03, -7.4915e-03],\n",
              "                        [-1.8098e-02, -2.4035e-02, -9.1030e-03]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.4084e-02,  2.3761e-02,  2.4476e-02],\n",
              "                        [ 1.8807e-02,  8.3184e-03, -1.7760e-02],\n",
              "                        [-2.0700e-02,  2.8366e-02, -1.9124e-02]],\n",
              "              \n",
              "                       [[ 2.5625e-02, -2.2975e-03,  5.7320e-03],\n",
              "                        [-1.6562e-02,  2.7421e-02, -2.2418e-02],\n",
              "                        [ 1.9587e-02, -2.9757e-03, -2.9004e-02]],\n",
              "              \n",
              "                       [[ 1.3298e-02,  1.9092e-02, -2.5138e-02],\n",
              "                        [ 2.3813e-02,  1.1220e-02, -7.9737e-03],\n",
              "                        [-2.4338e-02,  2.0697e-02, -1.0454e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.9760e-02, -1.8081e-02,  2.0861e-02],\n",
              "                        [ 2.2976e-02, -2.4337e-03,  2.0072e-02],\n",
              "                        [ 3.6633e-03,  1.3779e-02, -1.9008e-02]],\n",
              "              \n",
              "                       [[-8.5138e-03,  2.4469e-03,  9.6844e-03],\n",
              "                        [-1.3528e-02,  2.2917e-02, -1.2425e-02],\n",
              "                        [-2.0445e-02, -1.8041e-02, -2.8424e-02]],\n",
              "              \n",
              "                       [[-2.7625e-02, -3.4143e-03, -2.6520e-02],\n",
              "                        [ 1.7440e-02,  9.1712e-03, -2.6030e-02],\n",
              "                        [ 6.6198e-03,  2.1810e-02, -2.5993e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.6727e-02, -2.8145e-02, -1.6865e-02],\n",
              "                        [-1.6820e-02,  1.0475e-03, -2.0697e-02],\n",
              "                        [-1.9895e-03,  2.8004e-02, -1.6159e-02]],\n",
              "              \n",
              "                       [[ 7.5696e-03,  2.5783e-02,  2.1842e-02],\n",
              "                        [ 1.8630e-02, -2.0158e-03,  4.1715e-03],\n",
              "                        [-2.4146e-03,  8.7280e-04, -6.5161e-03]],\n",
              "              \n",
              "                       [[-9.5829e-03, -9.6489e-03, -1.9779e-02],\n",
              "                        [-5.4461e-03,  9.6429e-03, -2.7563e-02],\n",
              "                        [-1.1183e-02,  2.2451e-02, -1.2452e-02]]]])),\n",
              "             ('layer2.1.bn2.weight',\n",
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              "                      1., 1.])),\n",
              "             ('layer2.1.bn2.bias',\n",
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              "             ('layer2.1.bn2.running_mean',\n",
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              "                       0.0148, -0.0136, -0.0109, -0.0215,  0.0077,  0.0208, -0.0273,  0.0151,\n",
              "                       0.0093,  0.0071, -0.0145,  0.0078,  0.0012, -0.0152, -0.0176, -0.0464])),\n",
              "             ('layer2.1.bn2.running_var',\n",
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              "                      0.9100, 0.9154])),\n",
              "             ('layer2.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer3.0.conv1.weight',\n",
              "              tensor([[[[ 1.1741e-02, -1.1396e-03, -1.8223e-02],\n",
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              "                        [-2.1404e-02,  2.9206e-02,  1.2324e-02]],\n",
              "              \n",
              "                       [[ 1.8201e-02, -1.4778e-02,  2.3267e-02],\n",
              "                        [-7.0262e-03,  1.2071e-02, -1.8904e-02],\n",
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              "              \n",
              "                       [[ 1.4449e-03,  1.7082e-02, -2.7127e-02],\n",
              "                        [-8.0647e-03,  2.3011e-02, -2.3696e-02],\n",
              "                        [-1.1074e-03,  1.8588e-02, -2.8643e-04]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 2.0740e-02, -2.3646e-02, -1.5019e-02],\n",
              "                        [ 2.1705e-02,  1.8118e-02,  2.0896e-02],\n",
              "                        [ 6.2605e-03,  1.0173e-02,  1.7733e-03]],\n",
              "              \n",
              "                       [[ 2.2327e-02, -2.9203e-02,  1.8439e-02],\n",
              "                        [ 7.8299e-04, -2.5854e-02,  2.0558e-03],\n",
              "                        [ 1.4453e-02,  2.3752e-02, -4.5503e-03]],\n",
              "              \n",
              "                       [[-2.6120e-02,  2.1785e-02,  2.3275e-02],\n",
              "                        [-2.3707e-03,  2.3391e-02, -1.1406e-02],\n",
              "                        [ 1.8519e-02, -2.4153e-02,  2.5678e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.7439e-02, -1.8331e-02,  1.6992e-02],\n",
              "                        [ 9.4787e-03,  4.0798e-03, -1.8932e-02],\n",
              "                        [-1.2740e-02, -2.8425e-02, -2.2873e-02]],\n",
              "              \n",
              "                       [[-2.6060e-02,  7.5737e-03,  1.3130e-02],\n",
              "                        [ 1.7706e-02, -6.1058e-03, -3.2164e-03],\n",
              "                        [ 2.6385e-02,  2.7367e-02,  1.9837e-02]],\n",
              "              \n",
              "                       [[ 1.9116e-02,  2.1757e-03, -1.4174e-02],\n",
              "                        [ 1.7337e-02,  1.9596e-03,  8.2390e-03],\n",
              "                        [-1.0957e-02, -8.5255e-04, -1.2498e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.9206e-03, -1.4937e-02,  2.6094e-02],\n",
              "                        [-1.2027e-02,  2.7322e-03, -2.4787e-03],\n",
              "                        [-1.8288e-02,  1.3847e-02, -1.0115e-02]],\n",
              "              \n",
              "                       [[-1.0426e-02,  1.5089e-02,  1.7501e-03],\n",
              "                        [ 6.3758e-03,  2.6807e-02, -1.1175e-02],\n",
              "                        [ 1.9852e-02,  3.5554e-03,  1.6619e-02]],\n",
              "              \n",
              "                       [[ 3.7505e-03,  1.8900e-02,  1.8165e-02],\n",
              "                        [ 2.5376e-02, -1.5314e-02, -1.4934e-02],\n",
              "                        [-3.0512e-03,  1.3337e-02,  1.6371e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 1.6731e-02,  1.7874e-02, -1.7843e-02],\n",
              "                        [ 2.1816e-02, -1.9609e-02, -1.8188e-02],\n",
              "                        [ 1.9682e-02,  6.3026e-03, -2.2935e-02]],\n",
              "              \n",
              "                       [[-1.3628e-02,  1.2319e-02,  6.5821e-03],\n",
              "                        [ 3.8607e-03,  2.6806e-02, -2.6490e-02],\n",
              "                        [-2.0241e-02,  1.8058e-02,  2.1767e-03]],\n",
              "              \n",
              "                       [[-1.5588e-02, -6.4471e-03, -3.0626e-03],\n",
              "                        [-1.1534e-02,  8.4748e-03, -2.2271e-03],\n",
              "                        [-2.2442e-02,  8.9394e-03,  2.4114e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-8.5606e-03,  1.6958e-02,  1.0192e-02],\n",
              "                        [ 1.0822e-02,  1.9951e-02, -1.4363e-02],\n",
              "                        [ 2.8032e-02, -1.5157e-02, -1.9025e-02]],\n",
              "              \n",
              "                       [[ 1.0941e-02, -2.0728e-02, -1.1866e-02],\n",
              "                        [-1.4866e-02, -1.7987e-02, -2.1257e-02],\n",
              "                        [-1.6858e-02,  9.5946e-03,  2.5882e-02]],\n",
              "              \n",
              "                       [[-1.3893e-02, -2.5361e-02,  2.8702e-02],\n",
              "                        [ 2.1384e-02, -1.3311e-02, -1.5355e-02],\n",
              "                        [ 2.1760e-02,  1.1689e-02,  1.1261e-02]]],\n",
              "              \n",
              "              \n",
              "                      ...,\n",
              "              \n",
              "              \n",
              "                      [[[-2.2911e-02, -1.5840e-02, -1.6230e-02],\n",
              "                        [ 4.3951e-03, -2.3146e-02, -1.4517e-02],\n",
              "                        [ 8.4259e-03,  2.6574e-02, -2.5775e-02]],\n",
              "              \n",
              "                       [[ 2.4270e-02, -1.9798e-03, -1.1584e-02],\n",
              "                        [ 2.3168e-02,  2.1601e-02, -1.6067e-02],\n",
              "                        [ 5.4945e-03, -4.9988e-03,  2.0403e-02]],\n",
              "              \n",
              "                       [[-3.4399e-03, -5.6672e-03, -8.4560e-03],\n",
              "                        [-1.2915e-02,  1.4143e-02, -1.2023e-02],\n",
              "                        [ 2.7620e-02,  1.4014e-02, -2.3554e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.1684e-04,  1.8306e-02,  2.1371e-02],\n",
              "                        [ 3.8648e-03,  2.5848e-02,  1.8702e-02],\n",
              "                        [ 2.0200e-02,  3.2418e-03,  6.5558e-03]],\n",
              "              \n",
              "                       [[ 2.8718e-02,  1.2141e-02,  3.6103e-03],\n",
              "                        [-2.4501e-02,  1.8579e-03,  6.8367e-03],\n",
              "                        [ 7.2423e-03, -4.0544e-03, -1.3842e-02]],\n",
              "              \n",
              "                       [[ 2.0803e-02, -1.6996e-03, -1.0841e-02],\n",
              "                        [ 8.6220e-03, -2.3350e-03, -1.0005e-02],\n",
              "                        [ 6.4333e-03, -1.6576e-03, -1.5627e-02]]],\n",
              "              \n",
              "              \n",
              "                      [[[ 5.5071e-03,  2.6152e-02,  7.5349e-03],\n",
              "                        [-2.6042e-02,  8.1441e-03, -1.7147e-02],\n",
              "                        [-1.8942e-02,  9.7623e-04, -2.7801e-02]],\n",
              "              \n",
              "                       [[ 2.6142e-02, -9.7933e-03,  2.0406e-02],\n",
              "                        [ 5.8054e-03, -2.4073e-02, -1.2919e-02],\n",
              "                        [ 2.6491e-02, -2.6453e-02,  5.4403e-03]],\n",
              "              \n",
              "                       [[-8.4558e-03,  6.6834e-04, -7.1741e-03],\n",
              "                        [-1.9090e-02,  2.4863e-02, -2.8635e-02],\n",
              "                        [-7.4628e-03,  1.6309e-02,  2.6456e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-2.7081e-02, -2.2043e-02,  8.6747e-03],\n",
              "                        [ 9.6910e-04,  4.9983e-03,  1.0716e-02],\n",
              "                        [ 2.8251e-02,  1.2557e-02, -4.3007e-03]],\n",
              "              \n",
              "                       [[-1.1801e-02, -4.6192e-03,  1.1439e-02],\n",
              "                        [-1.5219e-02, -2.3537e-02,  2.5225e-02],\n",
              "                        [ 2.6820e-02, -6.3345e-03, -7.6817e-03]],\n",
              "              \n",
              "                       [[ 1.8999e-02,  8.0425e-03,  1.0730e-02],\n",
              "                        [ 1.4656e-02,  2.5031e-02,  9.7200e-03],\n",
              "                        [-3.6831e-03, -1.4022e-03,  2.6743e-02]]],\n",
              "              \n",
              "              \n",
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              "                        [-2.5422e-02,  1.5955e-04, -1.7885e-02],\n",
              "                        [ 1.3967e-02,  1.7990e-02, -1.1547e-02]],\n",
              "              \n",
              "                       [[-2.7833e-02,  1.5054e-02,  3.0901e-03],\n",
              "                        [ 6.0743e-03,  1.4876e-03,  1.1482e-02],\n",
              "                        [ 1.5961e-02,  2.1514e-02, -2.3124e-02]],\n",
              "              \n",
              "                       [[-5.7034e-03,  1.2676e-03,  1.6177e-02],\n",
              "                        [ 1.9539e-02,  1.6971e-02,  2.7916e-02],\n",
              "                        [-2.6372e-02, -2.4748e-02, -1.3277e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.6027e-02, -5.5433e-03, -1.4135e-02],\n",
              "                        [ 2.3304e-02,  1.2742e-02, -1.1356e-02],\n",
              "                        [-7.0400e-03,  1.1802e-02,  2.1310e-02]],\n",
              "              \n",
              "                       [[ 2.7954e-02,  1.1648e-02, -2.3633e-02],\n",
              "                        [ 2.5248e-02, -1.7045e-02, -2.0902e-02],\n",
              "                        [ 1.1526e-02, -1.3906e-02, -2.8123e-02]],\n",
              "              \n",
              "                       [[-2.5809e-02, -1.5543e-02,  2.5579e-02],\n",
              "                        [ 1.1476e-02,  1.8751e-02,  1.9613e-02],\n",
              "                        [-9.1550e-05,  1.2700e-02,  1.5279e-02]]]])),\n",
              "             ('layer3.0.bn1.weight',\n",
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              "             ('layer3.0.bn1.bias',\n",
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              "             ('layer3.0.bn1.running_mean',\n",
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              "                      -0.0201,  0.0060,  0.0220, -0.0448,  0.0554, -0.0377,  0.0104, -0.0553])),\n",
              "             ('layer3.0.bn1.running_var',\n",
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              "                      0.9295, 0.9269, 0.9308, 0.9348, 0.9343, 0.9341, 0.9271, 0.9352, 0.9333,\n",
              "                      0.9293, 0.9444, 0.9331, 0.9376])),\n",
              "             ('layer3.0.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer3.0.conv2.weight',\n",
              "              tensor([[[[ 4.2792e-04,  1.4352e-02,  3.5602e-03],\n",
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              "              \n",
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              "              \n",
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              "                        [-5.6787e-03,  8.2598e-03, -1.4279e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.9249e-02,  1.9510e-02, -3.1893e-03],\n",
              "                        [-6.8374e-03, -7.1622e-04,  1.6267e-02],\n",
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              "              \n",
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              "              \n",
              "                       [[ 1.4667e-02,  5.6510e-03, -8.5371e-04],\n",
              "                        [ 1.1009e-03, -1.2255e-02,  1.2364e-02],\n",
              "                        [-1.2006e-02,  1.6702e-02, -9.4721e-03]]],\n",
              "              \n",
              "              \n",
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              "                        [ 1.1111e-02, -1.8532e-03, -1.9835e-02],\n",
              "                        [ 1.5095e-02, -9.6375e-03, -1.3168e-02]],\n",
              "              \n",
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              "              \n",
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              "                        [ 1.4110e-02,  6.4044e-03,  1.9483e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.3091e-02,  1.0743e-03,  4.1510e-03],\n",
              "                        [ 1.8548e-02, -3.3138e-03,  2.7488e-04],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-7.4283e-03,  8.2564e-03, -1.3291e-02],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 1.4216e-02, -4.6013e-03,  8.4360e-03],\n",
              "                        [ 1.4603e-02, -1.6011e-02, -1.9954e-02]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[ 1.3364e-02,  1.2293e-02,  1.4393e-02],\n",
              "                        [ 9.4240e-03,  1.0468e-02,  7.2048e-04],\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [-1.2119e-02, -1.8479e-02, -1.9778e-03]],\n",
              "              \n",
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              "              \n",
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              "                        [-5.6502e-03,  1.1703e-02, -1.0319e-02],\n",
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              "              \n",
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              "              \n",
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              "                        [-3.4361e-03, -1.9963e-02, -1.3188e-02],\n",
              "                        [-1.5055e-02,  5.6074e-03, -1.4343e-02]]]])),\n",
              "             ('layer3.0.bn2.weight',\n",
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              "             ('layer3.0.bn2.running_mean',\n",
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              "                       2.9511e-03])),\n",
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              "                       0.0988,  0.0013, -0.0473, -0.0758,  0.0221, -0.0608, -0.0143, -0.0397])),\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "             ('layer3.1.bn1.weight',\n",
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              "             ('layer3.1.bn2.running_var',\n",
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              "             ('layer4.0.conv1.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                      0., 0., 0., 0., 0., 0., 0., 0.])),\n",
              "             ('layer4.0.bn1.running_mean',\n",
              "              tensor([ 2.0301e-02,  9.4844e-02, -7.6997e-03, -4.0971e-03, -5.2970e-02,\n",
              "                       4.4889e-02,  2.2398e-02,  3.0499e-02, -2.6216e-02, -5.0040e-02,\n",
              "                      -4.3384e-02, -2.5644e-02,  1.6738e-02,  3.6477e-03,  1.1129e-02,\n",
              "                      -8.6219e-02, -8.0657e-03,  2.8641e-02, -2.8634e-02,  6.8996e-02,\n",
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              "                      -4.6351e-02,  3.3683e-02, -1.4310e-03,  3.3435e-03,  6.2718e-02,\n",
              "                       2.8231e-02,  5.9878e-02, -2.2872e-02,  3.8468e-02, -3.2357e-02,\n",
              "                       3.9195e-02,  8.8477e-02, -2.1324e-02,  2.2230e-02, -5.2255e-02,\n",
              "                       2.4170e-02, -3.7943e-02,  9.2272e-03, -7.6639e-02,  3.2606e-02,\n",
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              "                       9.6385e-03,  9.0027e-03,  5.9839e-03,  3.5541e-02, -1.1176e-01,\n",
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              "                       1.7757e-02, -4.2960e-02, -2.5540e-02, -4.2438e-03,  2.0836e-03,\n",
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              "                      -8.7347e-02,  5.3389e-02,  1.9179e-03,  5.9854e-02, -2.2996e-02,\n",
              "                       6.1042e-02,  1.8410e-02,  2.4402e-02,  1.2799e-02, -1.3644e-02,\n",
              "                      -5.0214e-02, -3.2938e-02, -6.4136e-02, -7.7939e-02, -2.4552e-02,\n",
              "                       9.0158e-02, -3.9285e-02, -3.2100e-02, -6.0180e-02,  4.1476e-02,\n",
              "                       4.1197e-02,  8.7859e-02, -6.9071e-02,  5.4565e-02, -1.1037e-02,\n",
              "                       7.2374e-02, -1.3082e-02, -3.6918e-02,  6.1655e-03, -1.1466e-02,\n",
              "                      -1.0703e-02, -5.9211e-02])),\n",
              "             ('layer4.0.bn1.running_var',\n",
              "              tensor([0.9417, 0.9431, 0.9357, 0.9310, 0.9446, 0.9345, 0.9295, 0.9378, 0.9305,\n",
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              "                      0.9379, 0.9473, 0.9321, 0.9358, 0.9396, 0.9385, 0.9282, 0.9393, 0.9278,\n",
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              "                      0.9365, 0.9408, 0.9287, 0.9275, 0.9298, 0.9252, 0.9337, 0.9298, 0.9265,\n",
              "                      0.9274, 0.9419, 0.9348, 0.9338, 0.9293, 0.9275, 0.9303, 0.9409, 0.9296,\n",
              "                      0.9402, 0.9286, 0.9309, 0.9307, 0.9270, 0.9286, 0.9295, 0.9306, 0.9328,\n",
              "                      0.9346, 0.9312, 0.9334, 0.9299, 0.9297, 0.9358, 0.9482, 0.9288, 0.9336,\n",
              "                      0.9288, 0.9273, 0.9523, 0.9299, 0.9307, 0.9404, 0.9252, 0.9271, 0.9303,\n",
              "                      0.9247, 0.9330, 0.9370, 0.9272, 0.9310, 0.9306, 0.9420, 0.9293, 0.9296,\n",
              "                      0.9318, 0.9288, 0.9271, 0.9291, 0.9295, 0.9328, 0.9289, 0.9362, 0.9260,\n",
              "                      0.9291, 0.9315, 0.9378, 0.9250, 0.9305, 0.9313, 0.9364, 0.9280, 0.9356,\n",
              "                      0.9280, 0.9259, 0.9313, 0.9272, 0.9370, 0.9239, 0.9301, 0.9270, 0.9301,\n",
              "                      0.9404, 0.9289, 0.9295, 0.9303, 0.9233, 0.9328, 0.9299, 0.9375, 0.9308,\n",
              "                      0.9366, 0.9336, 0.9298, 0.9355, 0.9343, 0.9324, 0.9414, 0.9341, 0.9281,\n",
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              "             ('layer4.0.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                       1.6732e-02, -5.0148e-03,  9.2858e-03,  2.7815e-02, -2.3444e-02,\n",
              "                      -6.0194e-02,  1.8265e-02,  1.1030e-02,  2.4687e-02, -1.2254e-02,\n",
              "                       3.7453e-03, -1.0672e-02,  1.4545e-04, -9.2221e-04, -2.0345e-02,\n",
              "                      -3.7127e-02, -7.1267e-03, -1.2676e-02, -1.8355e-02,  3.7651e-03,\n",
              "                      -8.1616e-03, -9.1105e-03,  1.6689e-02,  2.9478e-02, -5.1353e-03,\n",
              "                      -1.6809e-02, -5.7434e-03,  1.0912e-02, -4.3560e-02,  7.2301e-04,\n",
              "                       1.0952e-02, -1.7034e-02,  9.1375e-04, -2.1840e-02, -1.3236e-02,\n",
              "                       1.9858e-02,  1.1881e-02,  1.3059e-02,  2.8184e-02,  9.3512e-03,\n",
              "                       2.9448e-02,  9.2364e-03, -1.3294e-02,  6.5076e-03, -2.0585e-03,\n",
              "                       2.0285e-02,  1.3714e-02,  2.1914e-02, -2.7892e-02, -2.2230e-03,\n",
              "                       2.1589e-02,  8.0544e-03, -1.2205e-02, -1.5897e-02,  1.9140e-03,\n",
              "                      -1.2607e-02, -3.2718e-03, -1.1733e-02, -1.1186e-02, -2.5680e-02,\n",
              "                       1.0144e-04,  1.1094e-02, -7.4530e-03,  3.2976e-03,  1.9393e-02,\n",
              "                      -2.5555e-03, -5.5970e-03,  1.3549e-02,  1.4509e-02, -1.7216e-03,\n",
              "                      -1.2712e-02,  1.3650e-02,  4.1189e-02,  2.8471e-02, -5.3430e-05,\n",
              "                       1.9156e-02,  6.8215e-03, -1.6975e-02,  1.2156e-02,  8.9761e-03,\n",
              "                      -6.1220e-04, -1.6765e-02, -1.7035e-02, -2.2025e-02,  6.7995e-03,\n",
              "                      -7.5107e-03,  1.3166e-03,  4.1009e-04, -1.4529e-02,  1.0152e-02,\n",
              "                      -1.8839e-02, -2.6568e-02,  1.2442e-02, -1.5716e-03,  1.0279e-02,\n",
              "                       2.5955e-02,  7.0276e-03, -1.6089e-02,  5.6997e-03,  2.2420e-02,\n",
              "                       7.5991e-03, -1.0487e-02,  1.4773e-02,  6.6278e-03,  1.0831e-02,\n",
              "                      -3.6424e-03, -2.5323e-02,  1.1058e-03, -2.6527e-03, -2.1900e-02,\n",
              "                       3.9636e-03,  1.7719e-02, -4.0334e-02,  2.3225e-03, -1.2650e-02,\n",
              "                       9.6586e-03,  2.5955e-02, -5.5607e-03,  2.4345e-02, -1.1265e-02,\n",
              "                      -3.6433e-03,  1.8139e-02, -8.3975e-03, -2.0079e-02, -1.8908e-02,\n",
              "                      -1.1950e-02, -1.7933e-02, -3.8434e-04,  1.4484e-02,  5.7888e-03,\n",
              "                      -2.2468e-03, -1.1012e-03,  2.0401e-02,  7.9926e-05, -3.0126e-03,\n",
              "                       1.8173e-02,  7.0158e-03,  4.8206e-03,  7.8228e-03, -1.3681e-02,\n",
              "                       5.6864e-03, -4.6154e-03, -2.7915e-02,  1.3552e-02,  3.9147e-03,\n",
              "                      -1.9235e-02,  3.3391e-02, -1.4450e-02, -9.8863e-03, -2.5781e-03,\n",
              "                      -3.2088e-02,  3.4483e-02,  1.9547e-02, -1.2716e-03, -1.5057e-02,\n",
              "                      -1.2456e-02,  1.7763e-02,  3.6301e-03,  1.1194e-02, -1.0014e-02,\n",
              "                       1.0282e-02, -1.2818e-02,  9.5218e-03,  6.7483e-03,  3.3926e-03,\n",
              "                      -5.3929e-03,  2.8176e-02,  6.2889e-03, -1.1006e-02, -2.2489e-02,\n",
              "                      -6.6644e-03,  1.5694e-02, -1.4443e-02,  1.6041e-02,  8.4744e-04,\n",
              "                      -1.1352e-02, -9.0924e-03, -3.6719e-03, -1.6357e-02,  2.0008e-02,\n",
              "                       6.9841e-03,  1.1806e-02,  5.7526e-03,  3.9244e-04,  2.3865e-02,\n",
              "                      -1.2052e-03,  8.8458e-03,  7.3758e-03, -1.5402e-02,  3.2503e-03,\n",
              "                       2.9044e-02, -2.4776e-02, -4.8223e-03, -3.2407e-02, -4.4252e-02,\n",
              "                       2.8373e-02,  9.6601e-03,  3.9149e-03,  1.8944e-02, -4.5354e-04,\n",
              "                      -1.1295e-02, -1.7623e-03,  5.0244e-03, -7.5533e-03,  1.6562e-02,\n",
              "                      -6.6173e-03,  2.6254e-02,  5.5996e-03, -7.4328e-03,  7.6868e-03,\n",
              "                      -1.3596e-02,  7.9533e-04, -1.3139e-02,  1.0835e-02, -5.7619e-03,\n",
              "                      -1.5479e-02, -6.3019e-03, -1.1526e-02, -2.5024e-02,  7.2337e-03,\n",
              "                       2.6178e-02,  8.2635e-03, -7.5682e-03, -1.4465e-02, -1.2264e-02,\n",
              "                       1.8472e-02, -8.6267e-05, -6.2649e-03,  2.7889e-02, -1.2573e-03,\n",
              "                       1.3195e-03, -2.3720e-02,  3.9646e-03, -2.6916e-04, -2.3955e-03,\n",
              "                      -1.7496e-02, -5.6745e-03,  9.9645e-05,  5.8410e-03, -8.6331e-03,\n",
              "                      -6.0303e-03, -8.4824e-03, -1.1355e-02,  2.0425e-03, -1.9383e-03,\n",
              "                      -4.6567e-03, -2.6214e-02,  2.4452e-02, -3.2762e-02,  2.5150e-02,\n",
              "                       1.3067e-02,  3.2285e-02,  3.6847e-03, -7.4390e-03,  1.8308e-02,\n",
              "                      -3.7575e-03, -5.3035e-03,  1.0465e-02, -5.2320e-03, -2.3919e-03,\n",
              "                       1.0846e-02,  1.0546e-02,  3.8753e-03,  1.4169e-02, -9.3475e-04,\n",
              "                       8.8144e-03, -2.6689e-02, -3.0612e-03, -1.2915e-03, -1.9239e-02,\n",
              "                       1.8817e-02,  2.6832e-02, -2.7235e-03,  2.7610e-03,  8.4100e-03,\n",
              "                       9.4400e-03,  1.6430e-02,  5.2646e-03,  3.8988e-02,  5.6952e-04,\n",
              "                      -2.0983e-02,  1.4692e-02, -3.0998e-04, -1.0440e-02,  7.5084e-03,\n",
              "                       9.5690e-03, -1.4369e-02,  3.1905e-02,  3.0965e-02,  3.3398e-03,\n",
              "                      -2.8129e-02, -3.8719e-03,  6.7154e-03,  1.0296e-02, -5.4592e-03,\n",
              "                       1.7316e-02, -4.7502e-03,  7.4104e-03,  1.5126e-02,  2.2739e-02,\n",
              "                       1.8596e-02,  9.0156e-03,  2.5824e-02,  7.9435e-03,  1.7121e-02,\n",
              "                      -2.5764e-02,  8.4242e-04,  3.0221e-02,  6.5350e-03,  5.6847e-03,\n",
              "                       7.3547e-03,  2.5871e-02, -1.1050e-02,  9.6254e-03,  1.8085e-03,\n",
              "                       4.0619e-03, -5.2260e-03, -1.9964e-02, -1.5340e-03,  1.4859e-02,\n",
              "                       1.0436e-02,  2.7597e-02,  1.2188e-02,  1.5339e-02, -1.0647e-03,\n",
              "                       1.0294e-02, -3.1130e-02,  2.1207e-02, -4.0594e-02,  1.4921e-03,\n",
              "                       5.9715e-05, -1.3305e-02,  6.2210e-03, -2.8644e-03, -3.4689e-03,\n",
              "                       3.2956e-04,  3.2039e-02, -1.4292e-02,  2.0387e-03, -1.2524e-02,\n",
              "                       7.8689e-03, -1.6327e-03, -4.4587e-02,  8.5328e-03, -4.6588e-03,\n",
              "                       2.9138e-03, -2.9471e-02,  2.6682e-05,  1.2845e-02, -4.0483e-03,\n",
              "                      -1.9512e-02, -9.6437e-04, -1.0861e-02, -1.5134e-02,  5.5516e-03,\n",
              "                      -4.7170e-02, -8.6759e-03,  1.5078e-02, -3.1936e-02,  1.2733e-02,\n",
              "                       9.1707e-03, -1.7706e-02,  2.1265e-03,  3.0565e-02,  2.9091e-02,\n",
              "                      -9.6269e-03, -2.6110e-02,  2.5481e-02,  5.1567e-03,  7.0206e-03,\n",
              "                       5.0818e-03, -3.0817e-02,  5.2528e-03, -9.1735e-03,  3.7812e-03,\n",
              "                       2.1683e-02,  1.7348e-02, -2.4946e-02, -3.2532e-03,  6.8994e-03,\n",
              "                       1.9571e-02, -1.5189e-02, -8.9964e-03, -5.3376e-03,  3.8094e-03,\n",
              "                       2.8668e-02,  1.0245e-02, -9.5609e-03, -8.3404e-03, -1.8341e-02,\n",
              "                      -3.0843e-02, -2.4910e-02, -7.8749e-03,  3.1404e-02,  9.6743e-03,\n",
              "                       2.1720e-04,  2.1033e-02, -1.9012e-02,  2.3651e-02, -1.8991e-02,\n",
              "                       4.4562e-02,  1.0683e-02, -2.6638e-02,  2.1633e-02,  1.6007e-03,\n",
              "                       1.6783e-02, -1.2825e-02,  1.4348e-02,  1.6576e-02, -1.8811e-02,\n",
              "                      -9.8445e-03,  6.4944e-03])),\n",
              "             ('layer4.0.bn2.running_var',\n",
              "              tensor([0.9086, 0.9121, 0.9099, 0.9086, 0.9104, 0.9092, 0.9107, 0.9140, 0.9072,\n",
              "                      0.9096, 0.9099, 0.9078, 0.9082, 0.9081, 0.9089, 0.9080, 0.9104, 0.9095,\n",
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              "                      0.9098, 0.9092, 0.9088, 0.9084, 0.9079, 0.9087, 0.9132, 0.9081, 0.9087,\n",
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              "                      0.9082, 0.9077, 0.9100, 0.9113, 0.9114, 0.9091, 0.9109, 0.9099, 0.9089,\n",
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              "                      0.9098, 0.9102, 0.9125, 0.9090, 0.9077, 0.9097, 0.9107, 0.9081, 0.9107,\n",
              "                      0.9085, 0.9101, 0.9108, 0.9090, 0.9090, 0.9078, 0.9080, 0.9122, 0.9075,\n",
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              "                      0.9104, 0.9113, 0.9140, 0.9116, 0.9097, 0.9093, 0.9095, 0.9123, 0.9083,\n",
              "                      0.9082, 0.9137, 0.9092, 0.9088, 0.9081, 0.9107, 0.9078, 0.9084, 0.9088,\n",
              "                      0.9083, 0.9103, 0.9137, 0.9129, 0.9095, 0.9096, 0.9109, 0.9103, 0.9099,\n",
              "                      0.9089, 0.9090, 0.9108, 0.9079, 0.9117, 0.9079, 0.9092, 0.9093, 0.9107,\n",
              "                      0.9082, 0.9107, 0.9086, 0.9092, 0.9100, 0.9140, 0.9084, 0.9089, 0.9081,\n",
              "                      0.9088, 0.9096, 0.9085, 0.9094, 0.9077, 0.9101, 0.9084, 0.9083, 0.9094,\n",
              "                      0.9119, 0.9089, 0.9072, 0.9087, 0.9089, 0.9138, 0.9121, 0.9074, 0.9094,\n",
              "                      0.9092, 0.9084, 0.9085, 0.9099, 0.9083, 0.9105, 0.9105, 0.9076, 0.9084,\n",
              "                      0.9080, 0.9086, 0.9114, 0.9082, 0.9092, 0.9101, 0.9091, 0.9103, 0.9092,\n",
              "                      0.9082, 0.9124, 0.9095, 0.9148, 0.9121, 0.9087, 0.9111, 0.9083, 0.9077,\n",
              "                      0.9089, 0.9087, 0.9086, 0.9090, 0.9093, 0.9081, 0.9089, 0.9086, 0.9084,\n",
              "                      0.9088, 0.9094, 0.9102, 0.9100, 0.9102, 0.9101, 0.9076, 0.9097, 0.9097,\n",
              "                      0.9082, 0.9084, 0.9092, 0.9098, 0.9106, 0.9140, 0.9102, 0.9088, 0.9113,\n",
              "                      0.9106, 0.9131, 0.9090, 0.9163, 0.9105, 0.9101, 0.9115, 0.9102])),\n",
              "             ('layer4.0.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.0.shortcut.0.weight',\n",
              "              tensor([[[[ 0.0339]],\n",
              "              \n",
              "                       [[-0.0492]],\n",
              "              \n",
              "                       [[-0.0318]],\n",
              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-0.0173]],\n",
              "              \n",
              "                       [[-0.0596]],\n",
              "              \n",
              "                       [[-0.0241]]],\n",
              "              \n",
              "              \n",
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              "              \n",
              "                       [[ 0.0484]],\n",
              "              \n",
              "                       [[ 0.0175]],\n",
              "              \n",
              "                       ...,\n",
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              "                       [[-0.0497]],\n",
              "              \n",
              "                       [[-0.0288]],\n",
              "              \n",
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              "              \n",
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              "                       [[ 0.0616]],\n",
              "              \n",
              "                       ...,\n",
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              "              \n",
              "                       [[-0.0109]],\n",
              "              \n",
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              "              \n",
              "              \n",
              "                      ...,\n",
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              "              \n",
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              "                       [[ 0.0243]],\n",
              "              \n",
              "                       [[-0.0215]],\n",
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              "                      0.9282, 0.9302, 0.9521, 0.9314, 0.9325, 0.9386, 0.9332, 0.9332, 0.9360,\n",
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              "                      0.9346, 0.9368, 0.9260, 0.9340, 0.9323, 0.9311, 0.9294, 0.9349, 0.9342,\n",
              "                      0.9368, 0.9323, 0.9304, 0.9283, 0.9302, 0.9334, 0.9368, 0.9332, 0.9352,\n",
              "                      0.9378, 0.9285, 0.9328, 0.9281, 0.9339, 0.9402, 0.9299, 0.9303, 0.9367,\n",
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              "                      0.9234, 0.9277, 0.9526, 0.9402, 0.9298, 0.9315, 0.9474, 0.9332, 0.9371,\n",
              "                      0.9339, 0.9310, 0.9322, 0.9357, 0.9356, 0.9338, 0.9388, 0.9330, 0.9302,\n",
              "                      0.9401, 0.9286, 0.9340, 0.9309, 0.9343, 0.9354, 0.9387, 0.9327, 0.9422,\n",
              "                      0.9264, 0.9329, 0.9475, 0.9350, 0.9358, 0.9318, 0.9272, 0.9297, 0.9352,\n",
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              "                      0.9333, 0.9310, 0.9378, 0.9302, 0.9333, 0.9422, 0.9312, 0.9315, 0.9283,\n",
              "                      0.9271, 0.9328, 0.9335, 0.9307, 0.9336, 0.9313, 0.9529, 0.9331, 0.9444,\n",
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              "                      0.9404, 0.9325, 0.9323, 0.9364, 0.9366, 0.9453, 0.9316, 0.9281, 0.9342,\n",
              "                      0.9344, 0.9355, 0.9292, 0.9312, 0.9424, 0.9343, 0.9285, 0.9320, 0.9435,\n",
              "                      0.9420, 0.9321, 0.9293, 0.9410, 0.9367, 0.9306, 0.9425, 0.9288, 0.9569,\n",
              "                      0.9374, 0.9292, 0.9309, 0.9329, 0.9364, 0.9350, 0.9320, 0.9293, 0.9336,\n",
              "                      0.9350, 0.9416, 0.9311, 0.9303, 0.9299, 0.9329, 0.9348, 0.9289, 0.9311,\n",
              "                      0.9385, 0.9307, 0.9234, 0.9337, 0.9307, 0.9397, 0.9292, 0.9266, 0.9292,\n",
              "                      0.9313, 0.9384, 0.9298, 0.9339, 0.9341, 0.9306, 0.9426, 0.9515, 0.9398,\n",
              "                      0.9346, 0.9418, 0.9314, 0.9370, 0.9392, 0.9324, 0.9336, 0.9299, 0.9339,\n",
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              "                      0.9309, 0.9327, 0.9337, 0.9299, 0.9488, 0.9306, 0.9341, 0.9316, 0.9267,\n",
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              "                      0.9336, 0.9275, 0.9334, 0.9338, 0.9333, 0.9347, 0.9335, 0.9365, 0.9300,\n",
              "                      0.9345, 0.9360, 0.9392, 0.9378, 0.9397, 0.9424, 0.9263, 0.9349])),\n",
              "             ('layer4.0.shortcut.1.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.1.conv1.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.3174e-02,  1.7638e-03, -6.3281e-03],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "                        [ 3.7493e-03, -3.7911e-03, -1.9010e-04]]]])),\n",
              "             ('layer4.1.bn1.weight',\n",
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              "             ('layer4.1.bn1.running_mean',\n",
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              "                      -4.9716e-02, -3.7653e-03])),\n",
              "             ('layer4.1.bn1.running_var',\n",
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              "                      0.9166, 0.9169, 0.9185, 0.9177, 0.9182, 0.9238, 0.9182, 0.9211])),\n",
              "             ('layer4.1.bn1.num_batches_tracked', tensor(1)),\n",
              "             ('layer4.1.conv2.weight',\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "                       ...,\n",
              "              \n",
              "                       [[-1.4315e-02,  8.9180e-03, -1.2910e-02],\n",
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              "              \n",
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              "              \n",
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              "              \n",
              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "              \n",
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              "             ('layer4.1.bn2.running_mean',\n",
              "              tensor([-3.0753e-02, -1.7548e-02, -2.2518e-02,  2.1388e-02, -2.7981e-02,\n",
              "                      -2.1918e-02,  1.9584e-02, -1.4510e-02, -1.2520e-02,  1.6949e-02,\n",
              "                       2.0188e-02, -5.7958e-03,  2.3857e-04, -1.6814e-02, -3.7807e-02,\n",
              "                       1.2872e-02, -1.2202e-02,  1.8070e-02,  9.8642e-03,  1.4184e-02,\n",
              "                       7.5270e-04,  9.0297e-03,  4.6316e-02,  7.3036e-03,  1.3818e-02,\n",
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              "                      -1.1023e-02,  2.0232e-03, -5.1439e-04, -4.5694e-03,  1.8357e-02,\n",
              "                       7.6622e-03,  9.5794e-03,  4.5498e-03,  1.1143e-02,  9.8385e-03,\n",
              "                       3.3199e-02, -6.5224e-03,  8.3967e-03, -6.7466e-03, -9.0760e-03,\n",
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              "                       3.0015e-02, -1.3779e-02,  6.2394e-03, -2.6073e-03, -1.3664e-02,\n",
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              "                      -1.5393e-02,  7.3834e-03,  2.3857e-02,  1.4748e-02,  2.9760e-02,\n",
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              "                      -1.0572e-02,  2.5367e-02,  8.6556e-03, -1.3246e-02,  9.2526e-03,\n",
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              "                      -2.0363e-03, -7.1241e-03,  1.0321e-02,  2.3568e-02,  3.2684e-03,\n",
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              "                       4.8717e-03,  8.4691e-03, -4.3863e-03, -1.3620e-02, -3.3281e-02,\n",
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              "                       1.1716e-02, -7.0957e-03, -1.7275e-02, -2.0225e-04,  7.6240e-04,\n",
              "                       2.1143e-02,  2.8189e-02, -1.3560e-02, -2.7829e-02,  6.8854e-04,\n",
              "                       3.2354e-03, -7.4977e-04, -1.9877e-02,  1.1524e-02,  1.3275e-02,\n",
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              "                      -1.8662e-02, -5.5705e-03,  2.8861e-02,  2.1974e-02, -1.2774e-02,\n",
              "                       1.8329e-02, -3.2700e-02,  2.5589e-03, -4.8114e-03,  2.3623e-02,\n",
              "                       1.0892e-02, -5.1535e-03, -9.8873e-03, -1.9509e-02, -1.9708e-02,\n",
              "                       1.1159e-02,  1.3025e-02, -1.7877e-02, -1.2175e-02,  4.1809e-03,\n",
              "                      -7.2986e-03, -6.2180e-03])),\n",
              "             ('layer4.1.bn2.running_var',\n",
              "              tensor([0.9092, 0.9077, 0.9093, 0.9113, 0.9095, 0.9100, 0.9112, 0.9083, 0.9090,\n",
              "                      0.9110, 0.9121, 0.9134, 0.9090, 0.9096, 0.9085, 0.9081, 0.9080, 0.9092,\n",
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              "                      0.9098, 0.9091, 0.9081, 0.9090, 0.9136, 0.9079, 0.9083, 0.9092, 0.9097,\n",
              "                      0.9092, 0.9128, 0.9078, 0.9104, 0.9107, 0.9089, 0.9082, 0.9099, 0.9125,\n",
              "                      0.9079, 0.9097, 0.9145, 0.9079, 0.9093, 0.9084, 0.9130, 0.9083, 0.9089,\n",
              "                      0.9082, 0.9107, 0.9105, 0.9107, 0.9100, 0.9083, 0.9089, 0.9108, 0.9091,\n",
              "                      0.9113, 0.9153, 0.9109, 0.9104, 0.9123, 0.9091, 0.9088, 0.9092, 0.9097,\n",
              "                      0.9097, 0.9081, 0.9082, 0.9123, 0.9082, 0.9090, 0.9100, 0.9097, 0.9098,\n",
              "                      0.9081, 0.9116, 0.9088, 0.9114, 0.9109, 0.9104, 0.9113, 0.9092, 0.9127,\n",
              "                      0.9091, 0.9090, 0.9083, 0.9095, 0.9102, 0.9100, 0.9091, 0.9075, 0.9083,\n",
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              "                      0.9079, 0.9133, 0.9106, 0.9079, 0.9101, 0.9105, 0.9084, 0.9094, 0.9094,\n",
              "                      0.9106, 0.9090, 0.9091, 0.9125, 0.9107, 0.9085, 0.9080, 0.9105, 0.9081,\n",
              "                      0.9106, 0.9107, 0.9099, 0.9090, 0.9077, 0.9091, 0.9133, 0.9098, 0.9093,\n",
              "                      0.9084, 0.9094, 0.9089, 0.9079, 0.9090, 0.9104, 0.9106, 0.9091, 0.9094,\n",
              "                      0.9090, 0.9124, 0.9094, 0.9098, 0.9087, 0.9115, 0.9081, 0.9090, 0.9088,\n",
              "                      0.9084, 0.9085, 0.9079, 0.9108, 0.9106, 0.9086, 0.9107, 0.9123, 0.9115,\n",
              "                      0.9117, 0.9098, 0.9089, 0.9083, 0.9115, 0.9101, 0.9084, 0.9093, 0.9102,\n",
              "                      0.9076, 0.9084, 0.9081, 0.9093, 0.9080, 0.9100, 0.9115, 0.9082, 0.9076,\n",
              "                      0.9122, 0.9094, 0.9099, 0.9090, 0.9129, 0.9106, 0.9147, 0.9086, 0.9093,\n",
              "                      0.9113, 0.9112, 0.9118, 0.9082, 0.9077, 0.9110, 0.9109, 0.9084, 0.9131,\n",
              "                      0.9090, 0.9096, 0.9077, 0.9099, 0.9096, 0.9077, 0.9119, 0.9089, 0.9082,\n",
              "                      0.9116, 0.9099, 0.9090, 0.9108, 0.9079, 0.9104, 0.9084, 0.9101, 0.9085,\n",
              "                      0.9081, 0.9083, 0.9077, 0.9120, 0.9081, 0.9080, 0.9104, 0.9138, 0.9107,\n",
              "                      0.9082, 0.9118, 0.9076, 0.9084, 0.9093, 0.9080, 0.9078, 0.9091, 0.9092,\n",
              "                      0.9083, 0.9097, 0.9082, 0.9090, 0.9095, 0.9117, 0.9095, 0.9085, 0.9117,\n",
              "                      0.9095, 0.9093, 0.9086, 0.9124, 0.9094, 0.9084, 0.9101, 0.9124, 0.9081,\n",
              "                      0.9114, 0.9087, 0.9098, 0.9081, 0.9104, 0.9095, 0.9137, 0.9089, 0.9095,\n",
              "                      0.9139, 0.9101, 0.9091, 0.9086, 0.9090, 0.9087, 0.9083, 0.9077, 0.9084,\n",
              "                      0.9096, 0.9088, 0.9086, 0.9095, 0.9084, 0.9101, 0.9099, 0.9125, 0.9107,\n",
              "                      0.9127, 0.9086, 0.9082, 0.9089, 0.9089, 0.9099, 0.9132, 0.9129, 0.9113,\n",
              "                      0.9073, 0.9135, 0.9102, 0.9092, 0.9096, 0.9092, 0.9126, 0.9081, 0.9116,\n",
              "                      0.9080, 0.9110, 0.9109, 0.9078, 0.9085, 0.9081, 0.9078, 0.9091, 0.9090,\n",
              "                      0.9090, 0.9099, 0.9129, 0.9086, 0.9087, 0.9087, 0.9101, 0.9083, 0.9087,\n",
              "                      0.9128, 0.9120, 0.9080, 0.9095, 0.9099, 0.9094, 0.9106, 0.9090, 0.9107,\n",
              "                      0.9075, 0.9109, 0.9106, 0.9092, 0.9113, 0.9093, 0.9080, 0.9085])),\n",
              "             ('layer4.1.bn2.num_batches_tracked', tensor(1)),\n",
              "             ('fc.weight',\n",
              "              tensor([[-0.0287,  0.0279,  0.0205,  ...,  0.0263,  0.0403,  0.0411],\n",
              "                      [-0.0227, -0.0201, -0.0244,  ..., -0.0341, -0.0159, -0.0007],\n",
              "                      [-0.0265,  0.0046, -0.0407,  ..., -0.0182,  0.0343, -0.0257],\n",
              "                      ...,\n",
              "                      [ 0.0089,  0.0336,  0.0096,  ..., -0.0276,  0.0287,  0.0004],\n",
              "                      [-0.0368,  0.0385,  0.0102,  ..., -0.0164,  0.0138, -0.0369],\n",
              "                      [ 0.0393,  0.0270,  0.0312,  ..., -0.0149, -0.0312,  0.0364]])),\n",
              "             ('fc.bias',\n",
              "              tensor([-0.0098, -0.0183,  0.0065, -0.0019,  0.0289, -0.0028, -0.0356, -0.0200,\n",
              "                       0.0014,  0.0224]))])"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.state_dict()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IN8ZwGB15NcU"
      },
      "source": [
        "# 设置交叉熵损失函数，SGD优化器"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:40.023837Z",
          "start_time": "2025-06-26T01:43:40.019952Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LedSNgyr5NcU",
        "outputId": "67ed646b-268e-40e9-bce8-69802fac18bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "损失函数: CrossEntropyLoss()\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# 定义损失函数和优化器\n",
        "loss_fn = nn.CrossEntropyLoss()  # 交叉熵损失函数，适用于多分类问题，里边会做softmax，还有会把0-9标签转换成one-hot编码\n",
        "\n",
        "print(\"损失函数:\", loss_fn)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:43:40.035848Z",
          "start_time": "2025-06-26T01:43:40.032419Z"
        },
        "id": "8fPVMyVF5NcU"
      },
      "outputs": [],
      "source": [
        "model = ResNet18()\n",
        "\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)  # SGD优化器，学习率为0.01，动量为0.9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.732814Z",
          "start_time": "2025-06-26T01:43:40.035848Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423,
          "referenced_widgets": [
            "5e41f97b5cb141159a30c5c3bb80aed6",
            "2350602e785c46bf91f8fb36d3a68fa1",
            "f84ee7e59f9c44939ab0621ed867f146",
            "bf028319cec04730813a0ec23c2abfe9",
            "4a2e0eab5337492aaf4217ccd3857602",
            "7fbbe94b250f4924bfbb0fd578bdb8d2",
            "914c907f3c4c47259db47bb87c20e5b4",
            "ae5eef9d754e44f29a5cac5a368b87dc",
            "9503c011fd6348f19fe7027f8c2924e4",
            "8c3a53cdbaec46a691bfe644b829fe49",
            "21002939145144889af0331c247ef0c1"
          ]
        },
        "id": "e4fwl-iD5NcU",
        "outputId": "2c66548d-a017-4bac-d452-0c55db0c142c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "使用设备: cpu\n",
            "训练开始，共训练35200步\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e41f97b5cb141159a30c5c3bb80aed6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/35200 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipython-input-27-996839849.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m model, history = train_classification_model(model, train_loader, val_loader, loss_fn, optimizer, device, num_epochs=50, \n\u001b[0m\u001b[1;32m      9\u001b[0m early_stopping=early_stopping, model_saver=model_saver, tensorboard_logger=None)\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/deeplearning_train.py\u001b[0m in \u001b[0;36mtrain_classification_model\u001b[0;34m(model, train_loader, val_loader, criterion, optimizer, device, num_epochs, tensorboard_logger, model_saver, early_stopping, eval_step)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m                 \u001b[0;31m# 梯度回传，计算梯度\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m                 \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m                 \u001b[0;31m# 更新模型参数\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    624\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m             )\n\u001b[0;32m--> 626\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    627\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    345\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m     _engine_run_backward(\n\u001b[0m\u001b[1;32m    348\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/autograd/graph.py\u001b[0m in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    821\u001b[0m         \u001b[0munregister_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_register_logging_hooks_on_whole_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 823\u001b[0;31m         return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    824\u001b[0m             \u001b[0mt_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    825\u001b[0m         )  # Calls into the C++ engine to run the backward pass\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"使用设备: {device}\")\n",
        "model = model.to(device) #将模型移动到GPU\n",
        "early_stopping=EarlyStopping(patience=5, delta=0.001)\n",
        "model_saver=ModelSaver(save_dir='model_weights', save_best_only=True)\n",
        "\n",
        "\n",
        "model, history = train_classification_model(model, train_loader, val_loader, loss_fn, optimizer, device, num_epochs=50,\n",
        "early_stopping=early_stopping, model_saver=model_saver, tensorboard_logger=None)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.737721Z",
          "start_time": "2025-06-26T01:45:37.732814Z"
        },
        "id": "uIqGEOAS5NcU"
      },
      "outputs": [],
      "source": [
        "history['train'][-100:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.741226Z",
          "start_time": "2025-06-26T01:45:37.737721Z"
        },
        "id": "GdWNKsyw5NcV"
      },
      "outputs": [],
      "source": [
        "history['val'][-1000:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.816716Z",
          "start_time": "2025-06-26T01:45:37.744941Z"
        },
        "id": "_CGgaNC35NcV"
      },
      "outputs": [],
      "source": [
        "plot_learning_curves(history, sample_step=500)  #横坐标是 steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "ExecuteTime": {
          "end_time": "2025-06-26T01:45:37.818553Z",
          "start_time": "2025-06-26T01:45:37.816716Z"
        },
        "id": "YjdIzbiH5NcV"
      },
      "outputs": [],
      "source": [
        "# 创建一个SevenZipFile对象，用于读取'./test.7z'压缩包\n",
        "a = py7zr.SevenZipFile(r'./test.7z', 'r')\n",
        "# 将压缩包中的所有文件解压到'./competitions/cifar-10/'目录下\n",
        "a.extractall(path=r'./competitions/cifar-10/')\n",
        "# 关闭SevenZipFile对象，释放资源\n",
        "a.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iAMSX1aW5NcV"
      },
      "outputs": [],
      "source": [
        "# 导入所需库\n",
        "import os\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision import transforms\n",
        "import tqdm\n",
        "\n",
        "# 定义测试数据集类\n",
        "class CIFAR10TestDataset(Dataset):\n",
        "    def __init__(self, img_dir, transform=None):\n",
        "        \"\"\"\n",
        "        初始化测试数据集\n",
        "\n",
        "        参数:\n",
        "            img_dir: 测试图片目录\n",
        "            transform: 图像预处理变换\n",
        "        \"\"\"\n",
        "        self.img_dir = img_dir\n",
        "        self.transform = transform\n",
        "        self.img_files = [f for f in os.listdir(img_dir) if f.endswith('.png')]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.img_files)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        img_path = os.path.join(self.img_dir, self.img_files[idx])\n",
        "        image = Image.open(img_path).convert('RGB')\n",
        "\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        # 提取图像ID（文件名去掉扩展名）\n",
        "        img_id = int(os.path.splitext(self.img_files[idx])[0])\n",
        "\n",
        "        return image, img_id\n",
        "\n",
        "# 定义预测函数\n",
        "def predict_test_set(model, img_dir, labels_file, device, batch_size=64):\n",
        "    \"\"\"\n",
        "    预测测试集并生成提交文件\n",
        "\n",
        "    参数:\n",
        "        model: 训练好的模型\n",
        "        img_dir: 测试图片目录\n",
        "        labels_file: 提交模板文件路径\n",
        "        device: 计算设备\n",
        "        batch_size: 批处理大小\n",
        "    \"\"\"\n",
        "    # 图像预处理变换（与训练集相同）\n",
        "    transform = transforms.Compose([\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize((0.4917, 0.4823, 0.4467), (0.2024, 0.1995, 0.2010))\n",
        "    ])\n",
        "\n",
        "    # 创建测试数据集和数据加载器\n",
        "    test_dataset = CIFAR10TestDataset(img_dir, transform=transform)\n",
        "    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n",
        "\n",
        "    # 设置模型为评估模式\n",
        "    model.eval()\n",
        "\n",
        "    # 读取提交模板\n",
        "    submission_df = pd.read_csv(labels_file)\n",
        "    predictions = {}\n",
        "\n",
        "    # 使用tqdm显示进度条\n",
        "    print(\"正在预测测试集...\")\n",
        "    with torch.no_grad():\n",
        "        for images, img_ids in tqdm.tqdm(test_loader, desc=\"预测进度\"):\n",
        "            images = images.to(device)\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs, 1) #取最大的索引，作为预测结果\n",
        "\n",
        "            # 记录每个图像的预测结果\n",
        "            for i, img_id in enumerate(img_ids):\n",
        "                predictions[img_id.item()] = predicted[i].item() #因为一个批次有多个图像，所以需要predicted[i]\n",
        "\n",
        "    # 定义类别名称\n",
        "    class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "    # 将数值标签转换为类别名称\n",
        "    labeled_predictions = {img_id: class_names[pred] for img_id, pred in predictions.items()}\n",
        "\n",
        "    # 直接创建DataFrame\n",
        "    submission_df = pd.DataFrame({\n",
        "        'id': list(labeled_predictions.keys()),\n",
        "        'label': list(labeled_predictions.values())\n",
        "    })\n",
        "    # 按id列排序\n",
        "    submission_df = submission_df.sort_values(by='id')\n",
        "\n",
        "    # 检查id列是否有重复值\n",
        "    has_duplicates = submission_df['id'].duplicated().any()\n",
        "    print(f\"id列是否有重复值: {has_duplicates}\")\n",
        "    # 保存预测结果\n",
        "    output_file = 'cifar10_submission.csv'\n",
        "    submission_df.to_csv(output_file, index=False)\n",
        "    print(f\"预测完成，结果已保存至 {output_file}\")\n",
        "\n",
        "# 执行测试集预测\n",
        "img_dir = r\"competitions/cifar-10/test\"\n",
        "labels_file = r\"./sampleSubmission.csv\"\n",
        "predict_test_set(model, img_dir, labels_file, device, batch_size=128)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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